Dissemination

In the aspect of smart grid which contains various kinds of intelligent devices, a huge exchange of information flow is needed with high reliability and low latency. As one of the key enabling technologies of emerging smart societies and industries, the Internet of things (IoT) has evolved significantly in both technologies and applications. Thus, there is an urgent need for rethinking the entire communication protocol stack for wireless IoT networks in smart grid. In this paper, we review various application scenarios, fundamental performance limits and potential technical solutions for high-reliability and low-latency (HRLL) wireless IoT networks. In this paper, we also discussed physical, MAC (medium access control), and network layers of wireless IoT networks, which all have significant impacts on latency and reliability. In that sense, this study supports “technology” and “awareness” aspects of the SMART-MLA project.

In order to add more flexibility in smart grid and make full use of renewable energy resources (RES), an accurate forecasting of power load generation and RES generation is needed to make day-ahead plan. The forecasting problem is handled using machine learning which usually requires a large amount of training samples to obtain an accurate learner. However, in real-world smart grid applications, it is hard to collect enough samples for training due to various reasons (storage limits, data transmission loss, etc). In this paper, we solve the data shortage problem by using multi-task learning (MTL). In MTL, related tasks learn jointly to improve generalization performance. To exploit the high learning speed of extreme learning machines (ELMs), we apply the ELM framework to the MTL problem. Firstly, we present the ELM based MTL problem in the centralized setting. Due to the fact that in smart grid, many data sets of different tasks are also geographically distributed, decentralized machine learning is also studied. In that sense, this study supports “technology” and “adoption” aspects of the SMART-MLA project.

In smart grid monitoring and control, some tasks require low latency in the process of data collection and control. To meet the extremely low latency constraints of industrial wireless control in critical applications, the wireless high-performance scheme (WirelessHP) has been introduced as a promising solution. While traditional wireless systems achieve high reliability through packet retransmissions, this would impair the latency. In this paper, a set of packet error rate (PER) tests is performed by applying concatenated Reed Solomon (RS) and convolutional codes (CC) to the WirelessHP physical layer. The results achieve level PER without retransmission. In that sense, this study supports “technology” and “awareness” aspects of the SMART-MLA project.

Smart grid is a large-scale network which contains millions ICT devices. The data volume increases explosively with the rapid development of Internet of Things (IoTs). In our SMART-MLA project, accurate forecasting result of power load consumption for each household and RES generation are required to make day-ahead plan. In that case, cloud computing is not feasible to handle a large amount of distributed data due to latency and bandwidth limitations. Thus, having data processed close to the sources and devices is the key to overcome these limitations. In this paper, we focus on the study of big data analysis with decentralized learning in large-scale networks. Two scenarios, i.e., disjoint data set and overlapping data set are studied. Comparison results show that communication load can be reduced significantly by the Fountain-based scheme for large-scale networks. In that sense, this study supports “technology” and “adoption” aspects of the SMART-MLA project.

In this paper, we propose a novel framework for low-cost link adaptation for spatial modulation multiple-input multiple-output (SM-MIMO) systems-based upon the machine learning paradigm. In our SMART-MLA project, data are collected from various kinds of devices, during the two-way communication between devices and control center, the transmission process is complicated and result in high cost. In this paper, we first convert the problems of transmit antenna selection (TAS) and power allocation (PA) in SM-MIMO to ones-based upon data-driven prediction rather than conventional optimization-driven decisions. Then supervised-learning classifiers (SLC) are developed to obtain their statistically-consistent solutions. Moreover, for further comparison we integrate deep neural networks (DNN) with these adaptive SM-MIMO schemes. Our simulation results show that the proposed method outperform many conventional designs with a significant lower complexity. In that sense, this study supports “technology” and “awareness” aspects of the SMART-MLA project.

In this paper, we propose a scalable Big Data framework that collects the data from smart meters and weather sensors, pre-processes and loads it into a NoSQL database that is capable to store and further process large volumes of heterogeneous data. Then, a set of Machine Learning (ML) algorithms are designed and implemented to determine the load profiles and forecast the electricity consumption for residential buildings for the next 24 hours. For the Short-Term Load Forecast (STLF), a Feed-Forward Artificial Neural Network (FF-ANN) algorithm with backtracking adjustment of the learning rate that extends and optimizes the Nesterov learning method is proposed. Its performance is compared with six algorithms, i.e. FF-ANN with well-known learning methods, namely Momentum and Nesterov, Non-linear AutoRegressive with eXogenous (NARX), Deep Neural Network (DNN), Gradient Tree Boosting (GTB) and Random Forests (RF) that are competitive and powerful ML algorithms which have been successfully used for load forecast. Hence, for STLF, the seven algorithms are executed simultaneously and the best one is automatically selected considering its accuracy in terms of Root Mean Square Errors (RMSE). The proposed methodology contains the steps required to implement the Big Data framework, i.e. data pre-processing, transformation and loading, the configuration of the ML algorithms for dimensionality reduction, clustering, STLF with different algorithms from which the Best Performant Algorithm (BPA) is automatically selected to provide STLF for the next 24 hours. The methodology is ultimately tested considering a real case of a residential smart building. In that sense, this study supports “technology” aspect of the SMART-MLA project.

Flexibility services in distribution grids are provided by aggregators. This study focuses on electric market mechanisms, which the aggregators are providing services, and roles of the players in such market mechanisms. In this regard, the most prevailing international market mechanisms and general trend are addressed along with the required coordination between transmission system operators (TSO) and distribution system operators (DSO) in such mechanisms. Computer simulation analysis are performed to illustrate the importance of this coordination particularly after the increase of PV solar generation in distribution grids. Finally, relevant legislations and current implementations in Turkey are investigated along with recommendations. The study addresses the following target of the SMART-MLA project: “Multi-layer aggregator concept needs new market polices, legislation and/or incentives schemes for SG infrastructure. By demonstrating that SMART-MLA can, under the right circumstances, give the desired effects, the pilot trials will demonstrate how such regulative bodies can be structured for the desired impact for adoption of SG technologies. Multi-layer aggregator concept needs new market polices, legislation and/or incentives schemes for SG infrastructure” (from the project proposal). In that sense, this study supports “market” and “adoption” aspects of the SMART-MLA project.

With high penetration of renewable generation and distributed energy resources (DERs), distribution systems are facing new operational challenges due to their intermittency and uncertainty. To cope with these challenges, distribution system operators (DSOs) can use market tools to enable more active system management and control using flexibility. Local flexibility markets (LFMs) provide opportunities to trade flexibility among DSOs and aggregators in an economically efficient way. LFMs play a significant role in supporting flexible, economic and reliable operation of distribution systems, which further facilitate the integration of renewables, sustainable energy resources, flexible loads at the distribution level and effectively accelerate the transition to a low carbon future. Studies have been carried out to define the concepts, design the mechanisms and formulate the market clearing methods of LFMs in the literature. Therefore, there is a need to classify and organize the literature on designs and market clearing methods of the LFM. This paper reviews the published papers on LFMs to help researchers have overall understanding of the concepts, formulations and clearing methods of LFMs. This paper addresses the following target of the SMART-MLA project: “Multi-layer aggregator concept needs new market polices, legislation and/or incentives schemes for SG infrastructure. By demonstrating that SMART-MLA can, under the right circumstances, give the desired effects, the pilot trials will demonstrate how such regulative bodies can be structured for the desired impact for adoption of SG technologies.” (from the project proposal). In that sense, this paper supports “market”, “adoption” and “awareness” aspects of the SMART-MLA project.

The electricity consumption will continue to increase despite the overall efforts and tendencies of changing the old appliances to less energy intensive ones. The advancements of Electric Vehicles (EV) and public mobility, electric heating, and the abundance of smart appliances that enhance the comfort of modern life lead to an increasing consumption trend. On the other hand, prosumers raising the quota of distributed generation and storage capacity will balance the electricity consumption trend. These changes at the consumption and generation level lead to the necessity to increase the awareness and incentive the consumers’ behavior to flatten the consumption curve and improve the savings. Such objectives could be reached by properly setting the Time-of-Use (ToU) tariff rates to encourage the consumption at off-peak hours when the rates are lower and unstress the grid loading. In this paper, we propose a methodology for setting the Time-of-Use (ToU) tariff rates and peak/off-peak intervals using big data technologies and machine learning, and verify the assumptions considering the large volume of consumption data of over 4200 residential consumers recorded in a smart metering implementation trail period that took place in Ireland from January to December 2010. We calculate the contribution to the peak/off-peak of the total consumption and use it in setting the ToU tariff rates starting from the flat tariff. Then, the consumers’ sensitivity to tariff change from flat to ToU is considered to identify the consumption change. The results show that using ToU instead of flat tariff, the peak is reduced in average by 5 to 7.5% and annual savings are around 4%. Also, by clustering the consumers a better allocation of the tariffs is possible. Thus, clustering is proposed considering the importance of the tariff allocation in Demand Side Management (DSM). In that sense, this study supports “technology”, “market”, “adoption” and “awareness” aspects of the SMART-MLA project.

In our SMART-MLA project, massive amount of data such as historical load profile for each household and environmental data need to be handled using large-scale machine learning. The alternating direction method of multipliers (ADMM) has recently been recognized as a promising approach for large-scale machine learning models. In this paper, we investigate the communication efficiency and running time of ADMM in solving the consensus optimization problem over decentralized networks. Firstly, we review the effort of random walk ADMM (W-ADMM), which reduces communication costs at the expense of running time. Then proposed parallel random walk ADMM (PW-ADMM) algorithm, where multiple random walks are active at the same time. Moreover, the intelligent parallel random walk ADMM (IPW-ADMM) algorithm is proposed to further reduce the running time. In our SMART-MLA project which consider various sources of data, the proposed method help us to process them in parallel and dramatically shorten the running time. In that sense, this study supports “technology” and “awareness” aspects of the SMART-MLA project.

Internet of Things (IoT) plays an important role in smart grid. Due to the broadcasting nature of wireless communication, signals in the IoT systems are more vulnerable to eavesdropping, the security issues of IoT system also result in concerns among customers and hence the secure communication over the IoT systems is one of the most pressing problems needed to be solved. In this paper, a general framework for enhancing the physical layer security (PLS) in the Internet of Things (IoT) systems via channel feedback is established with promising result. The study addresses the following target of the SMART-MLA project: “achieve high-reliable, low-latency, secure and performant data transmission and management with relational databases”. In that sense, this study supports the “awareness” aspect of the SMART-MLA project.

Nowadays, the massive deployment of distributed energy resources in distribution networks poses more operational challenges such as network congestion to distribution system operators. To cope with these challenges in an economically efficient way, the distribution system operator can utilize demand-side flexibility traded in the local flexibility market to manage the distribution network. In the existing distribution-level flexibility market frameworks, the market operator is assumed to have access to network parameters in order to ensure the market clearing solution being technically feasible from the operational point of view. To protect the privacy of network parameters, this paper proposes an alternating direction method of multipliers -based market clearing strategy, in which the market operator communicates with the distribution system operator to clear the market such that the market clearing solution respects network operation constraints without revealing network parameters to the market operator. In addition, in the existing flexibility bid formulations, the energy payback condition is determined without considering operation constraints of flexibility resources and the flexibility cost has not been considered. To fill this gap, this paper formulates an optimal flexibility bidding model for aggregators, which carefully models the energy payback condition and enables the aggregator to receive the maximum revenue with flexibility costs considered. This paper addresses the following target of the SMART-MLA project: “Multi-layer aggregator concept needs new market polices, legislation and/or incentives schemes for SG infrastructure.” (from the project proposal). In that sense, this paper supports “technology”, “market”, and “adoption” aspects of the SMART-MLA project.

The impressive advantages offered by of demand-side participation have accelerated deployment of demand response (DR) programs. However, the first step to attain the benefits of DR programs is to increase awareness level of the customers. This paper proposes a simple-but-efficient platform to enlighten the costumers on manifested merits of demand-side load control. The proposed platform is a web-based application which acquires the load profile of the customer, associated flexible appliances, and the customer preferences for using the appliances. In turn, presents the optimal operation schedule for flexible appliances and attained benefits from using the optimal schedule. To calculate the optimal operation schedule, a mixed-integer linear optimization model is devised where the decision variables are settings of flexible appliances, charge/discharge status and amount of storage device, charge/discharge status, and amount of electrical vehicle. The devised optimization engine is linked to a database to acquire required data for optimization which encompasses historical data for customer load, forecasts of renewables, ratings of customers’ flexible appliances, and subjected energy tariff. The attained optimal scheduling for the customer is then returned to the database. On the other hand, the database is linked to the web-based user interface to get the user preferences (write to the database) and represent the recommendation for optimal operation and attained benefits (read from database). To manage the links between web-based user interface, database, and optimization tool, proper linking application programming interfaces (APIs) are devised. The proposed platform is testified using real-world data and its effectiveness is assured by experimental studies. In that sense, this study supports “technology” and “adoption” aspects of the SMART-MLA project.

The consumption data from smart meters and complex questionnaires reveals the electricity consumers’ willingness to adapt their lifestyle to reduce or change their behaviour in electricity usage to flatten the peak in electricity consumption and release the stress in the power grid. Thus, the electricity consumption can support the enforcement of tariff and demand response strategies. Although the plethora of complex, unstructured and heterogeneous data is collected from various devices connected to the Internet, smart meters, plugs, sensors and complex questionnaires, there is an undoubted challenge to handle the data flow that does not provide much information as it remains unprocessed. Therefore, in this paper, we propose an innovative methodology that organizes and extracts valuable information from the increasing volume of data, such as data about the electricity consumption measured and recorded at 30 min intervals, as well as data collected from complex questionnaires. In that sense, this study supports “adoption” and “awareness” aspects of the SMART-MLA project.

The modern power systems continue to evolve, influenced by technological developments, regulatory policy modifications, climate and environmental issues. The changes are more apparent at distribution systems level by proliferation of intermittent renewable energy resources, introduction of demand-side participation, integration of transactive energy platforms, power electronics influx, and integration of portable energy procurement facilities. The changing landscape brings about challenges along with opportunities pertaining to active distribution system operation. One of the most critical opportunities is enabling flexibility measures at distribution level which can be realized through demand response (DR) programs. To address manifested merits of DR aggregation, this book chapter studies harvesting flexibility from DR aggregation through a three-layer approach: the first layer deals with consumer/prosumer stage as the first step to attain the benefits of DR programs is to increase awareness level of the customers; the second layer deals with customers’ demand control and flexibility management between aggregators and consumers; the third layer provides a competitive trading platform, i.e., local flexibility market (LFM) to facilitate flexibility trading between flexibility buyers (i.e., the distribution system operator (DSO)) and flexibility sellers (e.g., the aggregators representing customers). In the LFM, the market operator is assumed to have access to network parameters to ensure the market clearing solution being technically feasible from the operational point of view. To protect the privacy of network parameters, this chapter proposes an alternating direction method of multipliers -based market clearing strategy, in which the market operator communicates with the DSO to clear the market such that the market clearing solution respects network operation constraints without revealing network parameters to the market operator. In addition, in the existing flexibility bid formulations, the energy payback condition is determined without considering operation constraints of flexibility resources and the flexibility cost has not been considered. To fill this gap, this chapter formulates an optimal flexibility bidding model for aggregators, which carefully models the energy payback condition and enables the aggregator to receive the maximum revenue with flexibility costs considered. The case studies were conducted on the Roy Billinton Test System with electrical vehicles and heat pumps as flexibility resources. The results demonstrate that the proposed LFM framework is effective to perform day-ahead congestion management of distribution networks and is profitable to aggregators and end-users. This book chapter supports “TECHNOLOGY” and “ADOPTION” aspects of the SMART-MLA project.

The increasing penetration of distributed energy resources (DERs) in distribution networks poses new challenges to the secure and efficient operation of distribution networks. One major challenge is the network congestion caused by the noncoordinated operation of flexible demands, such as electrical vehicles (EVs) and heat pumps (HPs). In general, there are two categories of market-based day-ahead congestion management methods for distribution networks: price-based method and incentive-based method. In order to use the synergy of the two types of methods and resolve potential conflicts against regulations when one type of method is implemented solely, a coordination scheme of the two types of methods is proposed for efficient dayahead congestion management. In the proposed coordination scheme, the dynamic tariff (DT) method as a price-based method is used to partly resolve congestion firstly, and the scheduled reprofiling product (SRP) in the incentive-based method is used to deal with the remaining congestion afterward. By employing the coordination scheme, the distribution system operator (DSO) holds the profit neutral position in terms of congestion management, denoting that the DSO does not have the congestion management cost or revenue. Based on the DT model and SRP-based model, the coordination problem is formulated as a two-level non-convex mixed-integer non-linear programming (MINLP) model that is transformed into a one-level MINLP model with linear constraints and solved by a proposed particle swarm optimization (PSO)-based solution strategy. The Roy Billinton Test System (RBTS) was used to conduct case studies to validate the effectiveness of the proposed coordination scheme for dayahead congestion management in distribution networks. The case study results demonstrate that the proposed coordination scheme can efficiently resolve congestion by the combined use of the DTs and SRPs while ensuring that the DSO is in a profit neutral position. This paper supports “TECHNOLOGY” and “ADOPTION” aspects of the SMART-MLA project.

In Smart grids, the communication requirements on the rates and multiple access are high. To achieve the objective, multiple antenna (MIMO) is normally needed. As a hybrid MIMO architecture, beamspace multiple input multiple output (MIMO) can significantly reduce the number of required radio frequency (RF) chains in millimeter wave (mmWave) massive MIMO systems without obvious performance loss, in which, however, the number of users supported cannot be larger than that of RF chains. To break this fundamental limit, we introduce the concept of code-domain non-orthogonal multiple access (NOMA) into beamspace MIMO. A beam selection scheme is proposed first to maximize the sum-rate by utilizing the quasi-orthogonality of the beamspace channel. Furthermore, a low-complexity detection algorithm is developed to realize the transceiver design in mmWave communications using lens antennas. Finally, numerical results of the decoding complexity at receiver side are analyzed. Simulation results show that the proposed beamspace MIMO-NOMA in code domain can achieve higher spectrum and energy efficiency compared with the existing beamspace MIMO. In that sense, this study supports “technology” aspects of the SMART-MLA project.

In Smart Grids, especially these with P2P trade, the trade information may be stored in local caches. With high speed millimetre wave communications, it is important to study efficient strategies. In this paper, network densification and edge caching become effective approaches to reduce the burden on the fronthaul links and the content delivery latency for wireless communication systems. However, maximizing system spectral efficiency cannot directly provide any insight on their energy requirements/efficiency for cache-enabled millimeter-wave (mmWave) radio access networks (RANs). In this paper, we study the design of energy-efficient transceiver, consisting of analog and digital precoder/combiner, for the delivery phase of the downlink of cache-enabled mmWave RANs. Due to the non-convexity of the delivery rate and objective, the coupling between the digital and analog precoders/combiners, and the constant module constraint on the elements of analog precoders/combiners, the problem of interest is non-convex and hard to obtain the global optimal solution, even the local optimal solution. To this end, we first overcome these challenges one-by-one and then transform the original problem into tractable one. Finally, an algorithmic framework that converges to the Karush-Kuhn-Tucker solution with provable is developed to achieve the design of energy-efficient transceiver. Numerical results are provided to evaluate the performance of the proposed algorithm, where fully digital precoding is used as benchmark. In that sense, this study supports “technology” and “awareness” aspects of the SMART-MLA project.

Social activities may have significant impacts to power grid trades and communications. A novel social-tie motivated relay selection scheme is proposed for dynamic device-to-device (D2D) communications overlaying cellular networks. Using the non-edge cellular users to forward data, the proposed relay selection scheme can improve the transmission performance of the cell-edge users as an explicit benefit of D2D relays. Meanwhile, the effects of both the physical layer and social layer on the relay selection are jointly considered, where social ties are regarded as not only the motivation of relay services, but also the metric of security performance. Moreover, a generalized satisfaction index is introduced for designing a uniform quality of experience (QoE) criterion that can map different quality of service (QoS) metrics such as rate, throughput, delay, into a unified metric, and hence, is beneficial for the tradeoff between QoE and resource efficiency of relay selection. Furthermore, a dynamic optimization process is constructed for analyzing the effects of both the mobility of users and the randomness of channel on the relay selection, with the aid of the Lyapunov framework and drift-plus-penalty (DPP) algorithm. Finally, numerical results validate the effects of the proposed relay selection scheme. In that sense, this study supports “technology” and “awareness” aspects of the SMART-MLA project.

As the traffic of smart meters may be quite unpredictable, it is critical to study how traffic impact the network and energy in communications networks for smart grids. To boost energy saving for the general delay- tolerant IoT networks, a two-stage, and single-relay queueing communication scheme is investigated. Concretely, a traffic-aware N-threshold and gated-service policy are applied at the relay. As two fundamental and significant performance metrics, the mean waiting time and long-term expected power consumption are explicitly derived and related with the queueing and service parameters, such as packet arrival rate, service threshold and channel statistics. Besides, we take into account the electrical circuit energy consumptions when the relay server and access point (AP) are in different modes and energy costs for mode transitions, whereby the power consumption model is more practical. The expected power minimization problem under the mean waiting time constraint is formulated. Tight closed-form bounds are adopted to obtain tractable analytical formulae with less computational complexity. The optimal energy-saving service threshold that can flexibly adjust to packet arrival rate is determined. In addition, numerical results reveal that: 1) sacrificing the mean waiting time not necessarily facilitates power savings; 2) a higher arrival rate leads to a greater optimal service threshold; and 3) our policy performs better than the current state-of-the-art. In that sense, this study supports “technology” and “awareness” aspects of the SMART-MLA project.

The new potential of Distributed Energy Resources (DER), residential consumers, buildings, and prosumers in terms of controllable devices, self-generation, and storage makes them more active in the market encouraged by lower electricity prices. In addition, integrating a higher volume of volatile Renewable Energy Sources (RES) that unstress the public grid by local trading interactions is a desirable target of Local Electricity Markets (LEM). In this paper, we propose a blockchain trading mechanism to simulate the electricity transactions for 11 modern smart houses with more than 300 appliances, 8 roof- or faced-PV systems, and smart-metered 15-minute readings that form a small-size community. The electricity generated at the community level lowers the electricity bills and brings benefits for prosumers (sellers) and consumers (buyers). Several trading mechanisms for LEM transactions including auctions such as Uniform Price (UP), Pay-As-Bid (PAB), Generalized Second-Price (GSP), Vickrey-Clark-Groves (VCG) methods are implemented to evaluate the benefits and show their efficiency. After the market is initially cleared, an adjustment coefficient of the price is proposed for both sides (seller and buyer) to enlarge the trading potential at the community level using blockchain technology. It proves to bring excellent results to the LEM participants and enhance trading with outstanding benefits. In that sense, this study supports “technology”, “awareness” and "market" aspects of the SMART-MLA project.

As the number of electric vehicles (EV) increases, technical concerns deal with additional load introduced by charging of EVs connected to power distribution networks, are being discussed in the literature. Smart mechanisms such as incentivizing charging of EV’s to periods at minimum loading hours are being considered to mitigate additional loading effect of EVs on power grids. In this study, such effects and smart solutions are investigated through quantitative analyses. For this purpose, impact on EVs are simulated for a set of pilot distribution grids in Turkey. First, reference network models of the pilot regions, which do not include any EV load, are developed for the target year 2030. Then EV charging points in different technologies (slow and fast charging) are added to reference network models under two main scenarios; home charging support (HCS) and public charging support (PCS). Arrival times of EVs to the charging stations and state-of-charge (SOC) of EVs at arrival time are modelled with a stochastic approach. The effects of EVs on the pilot grids are quantified in terms of annual capacity factor (%), overloading (%) of the branches (transformers and lines), and voltage drop (%) at the substations. This paper supports “TECHNOLOGY” and “ADOPTION” aspects of the SMART-MLA project.

The increasing penetration of distributed energy resources in distribution networks poses new challenges to the secure and efficient operation of distribution networks. One major challenge is the network congestion caused by the non-coordinated operation of flexible demands, such as electrical vehicles and heat pumps. In general, there are two categories of market-based day-ahead congestion management methods for distribution networks: price-based method and incentive-based method. In order to use the synergy of the two types of methods and to resolve potential conflicts against regulations when one type of method is implemented solely, a coordination scheme of the two types of methods is proposed for efficient day-ahead congestion management. In the proposed coordination scheme, the dynamic tariff (DT) as a price signal is used to partly resolve congestion firstly, and the scheduled reprofiling product (SRP) as an incentive-based flexibility service product is used to deal with the remaining congestion. By employing the coordination scheme, the distribution system operator (DSO) holds the profit neutral position in terms of congestion management, denoting that the DSO does not have the congestion management cost or revenue. Based on the DT model and SRP-based model, the coordination problem is formulated as a two-level non-convex mixed-integer non-linear programming (MINLP) model that is transformed into a one-level MINLP model with linear constraints and is solved by a proposed particle swarm optimization-based solution strategy. The Roy Billinton Test System was used to conduct case studies to validate the effectiveness of the proposed coordination scheme for day-ahead congestion management in distribution networks. The case study results demonstrate that the proposed coordination scheme can efficiently resolve congestion by the coordination of the DTs and SRPs while ensuring that the DSO is in a profit neutral position. This paper supports “TECHNOLOGY” and “ADOPTION” aspects of the SMART-MLA project.

The progress of ICT technologies, day-ahead forecast, home energy management systems, implementation of smart meters, and Distributed Energy Sources (DER) enables new business opportunities for prosumers to locally trade the surplus via blockchain platforms leading to considerable advantages at the community level. The current research handles settlement similar to a centralized market that it is not necessarily the best solution for blockchain. Nonetheless, the settlement is essential as sellers and buyers perceive the attractiveness of the local trading through the market results. In this paper, we propose two novel and efficient settlement mechanisms (Global Balancing Settlement GBS and Splitting Settlement SS) for Peer-to-Peer (P2P) electricity exchange enhancing the performance of the classic Pairwise Settlement PS. These will be written as stored procedures embedded into the smart contracts along with auctioning procedures. The simulations are performed using a small residential community with 30% of the electricity that can be locally traded to lower the bills and unstress the public grid. The performance of the two proposed settlement methods is proved by the 14 scenarios that thoroughly indicate that GBS and SS provide better results for both sellers and buyers than PS. In the reference scenario, with GBS, sellers have the highest encashments with almost 4% more, whereas buyers encounter the lowest payments with almost 5% less than in case of the classic settlement. Starting from reference scenario, alternative scenarios are envisioned to extend the analyses and assess the performance of the settlement mechanisms. The highest gain is recorded with GBS mechanism: almost 8.8% for sellers and 6.5% for buyers. Another interesting outcome is that GBS is providing better results than SS. When deviations are small, SS provides almost 6% gain for both sellers and buyers, but when they increase, the gain is exceedingly small or none. In that sense, this study supports “awareness”, "adoption" and "market" aspects of the SMART-MLA project.

The increasing penetration of distributed energy resources in distribution networks poses new challenges to the secure and efficient operation of distribution networks. One major challenge is the network congestion caused by the non-coordinated operation of flexible demands, such as electrical vehicles and heat pumps. In general, there are two categories of market-based day-ahead congestion management methods for distribution networks: price-based method and incentive-based method. In order to use the synergy of the two types of methods and to resolve potential conflicts against regulations when one type of method is implemented solely, a coordination scheme of the two types of methods is proposed for efficient day-ahead congestion management. In the proposed coordination scheme, the dynamic tariff (DT) as a price signal is used to partly resolve congestion firstly, and the scheduled reprofiling product (SRP) as an incentive-based flexibility service product is used to deal with the remaining congestion. By employing the coordination scheme, the distribution system operator (DSO) holds the profit neutral position in terms of congestion management, denoting that the DSO does not have the congestion management cost or revenue. Based on the DT model and SRP-based model, the coordination problem is formulated as a two-level non-convex mixed-integer non-linear programming (MINLP) model that is transformed into a one-level MINLP model with linear constraints and is solved by a proposed particle swarm optimization-based solution strategy. The Roy Billinton Test System was used to conduct case studies to validate the effectiveness of the proposed coordination scheme for day-ahead congestion management in distribution networks. The case study results demonstrate that the proposed coordination scheme can efficiently resolve congestion by the coordination of the DTs and SRPs while ensuring that the DSO is in a profit neutral position. In that sense, this study supports “technology”, “adoption”, and “market” aspects of the SMART-MLA project.

With the rise of urbanization, cities around the world have embraced applications and benefits of leveraging advanced technologies to deliver a range of services while promoting efficient, environmentally friendly, and sustainable eco-systems. By harnessing technology to improve the quality of life of citizens, these advanced technological tools have become critical in transforming urbanized cities across the globe into smart cities. Universities in particular have served as an ideal platform to showcase smart applications to promote smart campuses. This paper presents METU Smart Campus Project which addresses necessary analysis and recommendations for the implementation of a smart and sustainable campus at METU. Currently no demand side management and demand response system are deployed in METU. For METU campus, the loads can be categorized as (1) Educational loads representing the load of educational areas and offices; (2) Residential loads representing the electrical consumption of dormitories. As a demand response program, curtailing building loads can be considered as the heating and cooling system is not electrical. Cost of such an approach includes load curtailment cost. Layer 1 of the tool that is being developed in the SMART-MLA project can be utilized by the university to investigate potential savings from demand response. In that sense, this study supports “awareness” and “adoption” aspects of the SMART-MLA project.

The electricity demand has become more and more active significantly contributing to the sustainable development of the power systems. In this era, the demand response or the electricity consumers’ reaction needs to be quick. Thus, the data processing is relevant for many parties such as consumers, suppliers, grid and market operators. Multiple activities such as the consumption monitoring, appliances scheduling, integration of local micro-generation, implementation of the advanced tariff scheme, electric vehicles and storage devices management are various data sources that are continuously generated and requires Big Data technologies. In this paper, we investigate a pipeline solution (data ingestion and stream processing) for demand response management considering the progress of sensors and communication systems that generate a large volume of data. In that sense, this study supports “technology” and “adoption” aspects of the SMART-MLA project.

The electricity retailers or suppliers promote business models focused on consumers-centred services except few of them. The reader could even doubt their interest in offering the most appropriate tariffs since lower tariffs imply lower revenue and consequently lower profit. No one-fit-for-all service can actively engage the electricity consumers. Most of the electricity consumers do not understand their consumption behaviour because they do not know it, they do not have real access to the consumption data. The current web portals do not share the consumption data with third parties, thus there is no market solutions. In this paper, we propose a distributed database architecture on blockchain technology for electricity transactions that innovate and increase the market competition in the era of digitalization enhancing the consumers awareness and transparent transitions. In that sense, this study supports “technology”, “market” and “adoption” aspects of the SMART-MLA project.

The generic term of electricity market is in fact a very complex sector of activity, being the result of the interaction of several markets, such as: the bilateral contracts market, the day-ahead market, the balancing market, intra-day market, and the market for ancillary services. In these circumstances, an electricity supplier needs to address the setting of the selling electricity price to the end consumers. The basic objectives to be pursued are as follows: return on sale for a period of at least one year, maintaining competition on the market, use of a user-friendly price system, financial insurance against fluctuations in the market, and ensuring a cash-flow corresponding to the good functioning of the firm. Considering these objectives, in this paper, we propose a data framework to handle electricity consumption data and a procedure to set the electricity price in competitive environment of the electricity markets. In this regard, a case study for an office building that also includes a restaurant is depicted. In that sense, this study supports “technology”, “market”, “adoption” and “awareness” aspects of the SMART-MLA project.

The centralized systems dominated the operation of electricity markets and other sectors all over the world. In those systems, only the electricity producers and suppliers could send their notifications to the market operator without direct interactions among the market participants. However, this trend is changing rapidly as a consequence of the new opportunities for the new actors such as prosumers and also because of the development of communities microgrids. Also, the sensors, the smart appliances energized on batteries and the communication technologies are enhancing a better control that encourages the transactions at the consumers/prosumers level or peer-to-peer (P2P) electricity transactions that require a specific platform based on the blockchain technology. In this paper, we identify the advantages and limitations of the blockchain solutions for the electricity trade that allow the interaction of multiple users and communities including the centralized market platform fostering the market competition in the era of digitalization and consumers awareness. We also propose a blockchain platform architecture for P2P electricity transactions. In that sense, this study supports “technology”, “market”, “adoption” and “awareness” aspects of the SMART-MLA project.

This paper describes a business model innovation approach to multi-layer aggregation in smart power grids. Multi-layer aggregation makes it possible to sell and buy electrical power in the local market. To make this feasible, it is necessary to innovate an efficient business model and technology to handle the transactions between sellers and buyers. The proposed business model will use blockchain and smart contracts to handle transactions between the actors. In that sense, the study supports “market” and “adoption” aspects of the SMART-MLA project.

Recently, large volumes of electricity consumption data are pouring constantly from smart meters and other sensors that count for millions or even milliards of records. Our purpose in this paper is to handle such data and extract valuable information until it becomes stale. Sometimes, additional data such as meteorological, motion-sensitive, door position data, results from surveys, tariffs, etc. come together with the electricity consumption and increase the number of records. In this case, NoSQL solutions are utilized to process and analyze the entire volume of data. In this paper, we propose a data processing framework for electricity data set that comes from a trial smart metering implementation period that took place from 1st January to 31st December 2010 in Ireland. The main purpose is to cluster the consumers based on similarities regarding theirs 30- minute consumption, show their impact on the electricity consumption peak that could be used as an input in establishing real-time tariffs based on peak coefficient. In that sense, this study supports “technology” and “market” aspects of the SMART-MLA project.

One of the most important and well-known characteristics of the electricity is the necessity to balance almost perfectly the load with generation for each moment of the power system operation. This characteristic, coming from the very beginnings of the power systems design and building, has shaped the operational rules of the power systems and more recently has also shaped the electricity markets structure. The basic parameter used for designing various electricity markets is time. Also, the recent developments of renewable energy lead to a higher complexity of the power systems operation and as a direct consequence a higher complexity of the electricity market structure. Hence, nowadays, it is very difficult for a generation owner to optimize the operation of his asset from all points of view: technical, financial, etc. The main objective of this paper is to develop an electricity market simulator that includes the basics of a good practice guide starting from the use cases for generators or producers that compete on different electricity markets in order to maximize their financial results and efficiency. Thus, this paper mainly proposes to uncover the functionalities a simulator should have to assist market players to access different electricity markets. In that sense, this study supports “technology” and “market” aspects of the SMART-MLA project.

In this paper, we propose a big data solution architecture for the efficient operation of the microgrids that have emerged as a consequence of distributed generation, storage systems and advances of ICT technologies. The main goal is to develop a smart adaptive platform for Big Data analytics for microgrids efficient operation that involves monitoring and control of electrical appliances, generation and storage activities, demand response and market mechanisms. The platform essentially necessitates Big Data solutions that will process, manage and analyze large volumes of data generated by microgrids and modern appliances (IoT & sensors), small- and mid-scale generators based on renewable energy sources such as photovoltaic panels (PV) or micro-wind turbines which are integrated with storage devices (banks of batteries), smart loads, Electric Vehicles (EV) stations, settlement mechanisms and market trading activities. In that sense, this study supports “technology” aspect of the SMART-MLA project.

Owning several types of generating units requires an optimized schedule to cover the negotiated bilateral contracts. This approach will lead to a better electricity market strategy and benefits for an electricity producer. In this paper, we simulate the operation of five different generators including generators based on Renewable Energy Sources (such as wind turbines and photovoltaic panels) that belong to an electricity producer. The five generators are modelled considering the specificity of their type and primary energy source. For instance, for renewable energy sources, we will consider the 24-hour generation forecast. The objective function of the optimization process is to obtain an optimal loading of generators, while the constraints are related to the capacity and performance of the generators. The output consisting in a generating unit optimized operation schedule will be further used for day-ahead or balancing market bidding process. Hence, the producer will be able to adequately bid on the future electricity markets knowing the commitment of generators for negotiated bilateral contracts market. The simulations are tested for more than five generators considering the connection to a relational database where more data for generators is stored. In that sense, this study supports “technology” and “market” aspects of the SMART-MLA project.

Blockchain is a promising technology for local trading of the electricity. It has specific components, such as smart contracts, data ledger, consensus, and provides many benefits for both buyers and sellers because they are obtaining/generating electricity at better prices compared with the electricity from the public grid. This practice leads to a better integration of renewable energy sources, increasing the appetite for new local generation sources and storage facilities, transparency and trading opportunities for all market players. Grid operators also benefit from blockchain since the grid loading will be reduced as the grid does not have to transmit or distribute electricity from large power plants located far away from consumption place. In the end, the market players will benefit from reducing the grid loading and alleviating the congestions as onerous investment in grid infrastructure is avoided. In this paper, we analyse the advantages of different electricity market mechanisms for trading and settlement. Several auction mechanisms such as pay-as-bid, uniform price, generalised second price or Vickrey-Clarke-Groves are taken into account as feasible options for local markets and peer-to-peer trading. In that sense, this study supports “technology”, “market”, “adoption” and “awareness” aspects of the SMART-MLA project.

Most of the Renewable Energy Sources (RES) and changing consumption patterns bring more volatility and fluctuations for retailers and distribution system operators. Such challenges are expected to increase in the coming years as the policies are constantly promoting RES. The Local Flexibility Market (LFM) is envisioned as an innovative solution to control the imbalances that usually appear between contractual agreements and short-term forecast. Consumers' flexibility to control their programmable appliances and prosumers' flexibility to control the Distributed Energy Resources (DER) including distributed generation, controllable loads, batteries, etc. and urban buildings have to be rewarded as they substantially contribute to the imbalance reduction. Hence, the LFM encourages investment in flexibilities such as controllable devices, smart plugs and storage facilities fostering the market competition. The communication system between consumers/prosumers and grid operators, and the connectivity of controllable appliances are essential to make such markets operational. In this context, ICT platforms offer secure trading opportunities to the local flexibility market players such as consumers, prosumers, retailers and DSO. In this paper, we focus on the ICT architecture for trading the flexibilities. In that sense, this study supports “technology”, “market”, “adoption” and “awareness” aspects of the SMART-MLA project.

Nowadays the electricity consumption optimization represents a big improvement point for the electricity supplier, but also for the consumers. Both sides can benefit from the progress of sensors and ICT technologies and gain benefits if an automatically process is put in place. Hence, in this paper, we propose an algorithm which will monitor the electricity consumption and provide optimizations for each consumer, all in real time. For accurate monitoring outputs and better computation, the algorithm will run into a smart grid environment, where smart meters, actuator and appliances can be found and easily integrated. The proposed solution will be deployed in an edge computing environment. This architectural decision will make the final implementation more performant and less costly. In that sense, this study supports “technology” aspect of the SMART-MLA project.

The commercial buildings generate a significant volume of data that can be processed to assess the flexibility of the electricity consumption and their potential contribution to flatten the load curve or provide ancillary services. With the constant increase of the volatile output of the Renewable Energy Sources (RES) and numerous Electric Vehicles (EV), the flexibility potential of the commercial buildings has to be investigated to create smarter green cities. However, the volume of consumption data is significantly increasing when various activities are profiled, such as cooling, heating, fans, lights, equipment, etc. In this paper, we propose a big data processing framework or methodology to extract interesting insights from very large datasets and identify the flexibility of the commercial buildings (of several types from the United State of America – U.S.A.) and its market value in correlation with the Demand Response (DR) capabilities at the state and Independent System Operator (ISO) level. This is a theoretical approach combining several aspects, such as: large datasets processing techniques, DR programs, consumption data, flexibility potential estimation, scenarios and DR enabling technologies costs. Applying one of the DR programs, significant results in terms of savings are revealed from simulations. In that sense, this study supports “MARKET” and "ADOPTION" aspects of the SMART-MLA project.

This paper shows how the Ethereum blockchain can register settlements between an aggregator and prosumers in a smart grid. By providing flexible use of electricity to the aggregator, customers get rewarded. The flexibility is valuable for the aggregator since the power infrastructure may be used more efficiently. Blockchain is an exciting technology for handling settlements which, however, also has some clear limitations. For example, the cost per transaction on the public Ethereum blockchain is too high compared to the value of the actual transactions. A private blockchain is an alternative but removes some of the original benefits of using the public blockchain. The paper concludes that blockchain is a promising technology, and a private blockchain is more suitable for transactions containing minimal amounts. This paper supports “MARKET” and “ADOPTION” aspects of the SMART-MLA project.

Researchers and practitioners alike have discussed Vehicle-to-Grid (V2G) technology for quite some time, and we have extensive coverage of the technology needed, as well as opportunities, challenges, algorithms, and business models. However, few studies have examined V2G from a crisis management perspective. This paper presents a review of the current V2G literature. It gives an overview of the possibilities for using electric cars as a backup power supply in case of emergencies, such as citywide power outages. We calculate the potential power available in a typical Norwegian mid-sized city and examine to what extent this can be part of crisis management in case of a massive power outage. We then review existing business model literature to analyze possible incentives for people to make their cars available for V2G. Finally, we conclude by pointing out several technical and social issues that need to be addressed for V2G to become a viable option and suggest vehicle-to-home (V2H) as the most likely scenario in the short to mid-term. This paper supports “MARKET” and “ADOPTION” aspects of the SMART-MLA project.

The energy transition from fossil resources to integration of more renewables such as solar and wind has become the focus in the energy strategies of many countries. The time difference between solar energy production and power demand peak hours in the grid can be significant, bringing the role of electricity storage, especially battery systems, to center stage. Based on this fact with data from the solar energy output at Oslo and Nord Pool electricity prices, the revenue potential for storage is calculable. For the prosumers acting as both energy users and producers, storage is installed mainly for selfconsumption. In comparison, storage with aggregators may achieve profit out of the outrage of the market price fluctuation. In that sense, this study supports “adoption” and “market” aspects of the SMART-MLA project.

In this paper, we present the findings from a pilot survey on attitudes towards incentives for allowing third parties to control electricity use in households as part of the change to smart, green, and sustainable power grids. The survey was aimed at early adopters of smart home technology and shows that for this group, there is significant resistance towards allowing a third party to control household electricity use, at least unless the monetary incentive is high. However, early adopters are positive towards using smart home technology to lower their electricity bill if they stay in control. In that sense, this study supports “adoption” and “market” aspects of the SMART-MLA project.

Climate changes and environmental degradation require more attention and measures to increase energy efficiency and utilize more renewable energy sources. Traditional electricity consumers can produce and sell energy by implementing new technology, such as photovoltaic (PV) modules and windmills, making consumers prosumers of energy. This paper investigates the new prosumer business model and how it impacts value offerings by the distribution system operator (DSO) and the aggregator role in smart grids. From the power industry perspective, the aggregator can be seen as a virtual power plant in the smart grid system. The practical business opportunities for the aggregator are also explored. In that sense, this study supports “adoption” and “market” aspects of the SMART-MLA project.

An increasing number of prosumers participate in the energy market, either by offering flexibility or selling surplus energy. This is made possible through EU directives for electricity transactions in smart grids. The directives provide guidelines for individual and aggregated transactions, allowing customers to sell or share electric surplus at the local level or to the national grid. This is seen as an important part of the transition to renewable energy. In this paper, we introduce blockchain as a mechanism for handling decentralized transactions in smart grids. Blockchain technology allows for a flexible peer-to-peer trading mechanism. It can handle transmission and distribution management with energy flow optimization and grid infrastructure security, prosumer and microgrid management with different trading and pricing mechanisms, and interactive load between electric vehicles and grid. We describe blockchain technology, provide a survey of blockchain applications in the energy sector, emphasize the achievements and limitations of this technology in EU research studies and industrial projects, and underline the findings of the Smart-MLA project in this field. In that sense, this study supports “adoption” and “market” aspects of the SMART-MLA project.