Data analytics for profiling low‐voltage customers with smart meter readings

The energy transition for decarbonization requires consumers’ and producers’ active par-ticipation to give the power system the necessary flexibility to manage intermittency and non‐pro-grammability of renewable energy sources. The accurate knowledge of the energy demand of every single customer is crucial for accurately assessing their potential as flexibility providers. This topic gained terrific input from the widespread deployment of smart meters and the continuous development of data analytics and artificial intelligence. The paper proposes a new technique based on advanced data analytics to analyze the data registered by smart meters to associate to each customer a typical load profile (LP). Different LPs are assigned to low voltage (LV) customers belonging to nominal homogeneous category for overcoming the inaccuracy due to non‐existent coincident peaks, arising by the common use of a unique LP per category. The proposed methodology, starting from two large databases, constituted by tens of thousands of customers of different categories, clusters their consumption profiles to define new representative LPs, without a priori preferring a specific clustering technique but using that one that provides better results. The paper also proposes a method for associating the proper LP to new or not monitored customers, considering only few features easily available for the distribution systems operator (DSO)

Optimal Location of Energy Storage Systems with Robust Optimization

The integration of intermittent sources of energy and responsive loads in distribution system make the traditional deterministic optimization-based optimal power flow no longer suitable for finding the optimal control strategy for the power system operation. This paper presents a tool for energy storage planning in the distribution network based on AC OPF algorithm that uses a convex relaxation for the power flow equations to guarantee exact and optimal solutions with high algorithmic performances and exploits robust optimization approach to deal with the uncertainties related to renewables and demand. The proposed methodology is applied for storage planning on a distribution network that is representative of a class of networks

Strategic decision-making support for distribution system planning with flexibility alternatives

The ongoing power system transformation requires rethinking the planning and operation practices of the different segments to accommodate the necessary changes and take advantage of the forthcoming opportunities. This paper concerns novel approaches for appraising initiatives involving the use of flexibility from grid-connected users. This paper proposes a Decision Theory based Multi-Criteria Cost-Benefit Analysis (DT-MCA-CBA) methodology for smart grid initiatives that capture the complexity of the distribution system planning activities in which flexibility competes with grid expansion. Based on international guidelines, the proposed DT-MCA-CBA methodology systematically assesses tangible and intangible impacts, considering multiple conflicting criteria. The DT-MCA-CBA methodology relies on a novel approach that combines MCA and Decision Theory to identify the most valuable option in a complex decision-making problem by modelling the stakeholder perspective with the MiniMax regret decision rule. The proposed DT-MCA-CBA methodology is applied to a comparative case study concerning four different approaches for distribution system planning. A web-based software which implements the proposed decision-making framework and the DT-MCA-CBA methodology is developed to provide a novel decision-making support tool for strategical smart distribution system planning

Emerging business models in local energy markets: A systematic review of peer-to-peer, community self-consumption, and transactive energy models

The emergence of peer-to-peer, collective or community self-consumption, and transactive energy concepts gives rise to new configurations of business models for local energy trading among a variety of actors. Much attention has been paid in the academic literature to the transition of the underlying energy system with its macroeconomic market framework. However, fewer contributions focus on the microeconomic aspects of the broad set of involved actors. Even though specific case studies highlight single business models, a comprehensive analysis of emerging business models for the entire set of actors is missing. Following this research gap, this paper conducts a systematic literature review of 135 peer-reviewed journal articles to examine business models of actors operating in local energy markets. From 221 businesses in the reviewed literature, nine macro-actor categories are identified. For each type of market actor, a business model archetype is determined and characterised using the business model canvas. The key elements of each business model archetype are discussed, and areas are highlighted where further research is needed. Finally, this paper outlines the differences of business models for their presence in the three local energy market models. Focusing on the identified customers and partner relationships, this study highlights the key actors per market model and the character of the interactions between market participants

Corrigendum to “Emerging business models in local energy markets: A systematic review of peer-to-peer, community self-consumption, and transactive energy models” [Renew Sustain Energy Rev 179 (2023) 113273]

Schwidtal, J. M., Piccini, P., Troncia, M., Chitchyan, R., Montakhabi, M., Francis, C., Gorbatcheva, A., Capper, T., Mustafa, M. A., Andoni, M., Robu, V., Bahloul, M., Scott, I. J., Mbavarira, T., España, J. M., & Kiesling, L. (2023). Corrigendum to “Emerging business models in local energy markets: A systematic review of peer-to-peer, community self-consumption, and transactive energy models” [Renew Sustain Energy Rev 179 (2023) 113273](S1364032123001296)(10.1016/j.rser.2023.113273). Renewable and Sustainable Energy Reviews, 185(October), [113523]. https://doi.org/10.1016/j.rser.2023.113523 ---Funding Information: Ian Scott was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project - UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS. Funding Information: Merlinda Andoni received funding from the EPSRC DecarbonISation PAThways for Cooling and Heating (DISPATCH) project (grant number EP/V042955/1 ) and the InnovateUK Responsive Flexibility (ReFLEX) project [ref: 104780]. Funding Information: Valentin Robu was supported by the project “TESTBED2: Testing and Evaluating Sophisticated information and communication Technologies for enaBling scalablE smart griD Deployment”, funded by the European Union Horizon2020 Marie Skłodowska-Curie Actions ( MSCA ) [Grant agreement number: 872172 ].The authors regret that there were developments in affiliations and funding acknowledgements during the time from initial submission to final acceptance which have not been reported correctly. Specifically, the affiliations of the co-authors R. Chitchyan, M. Montakhabi, M. Andoni, and I. Scott were not up to date. The corrected affiliations of all authors are as follows. Concerning funding acknowledgements, the information of M. Andoni, V. Robu, and I.J. Scott were not up to date. In addition to the provided information, the following to fundings should be acknowledged. Merlinda Andoni received funding from the EPSRC DecarbonISation PAThways for Cooling and Heating (DISPATCH) project (grant number EP/V042955/1) and the InnovateUK Responsive Flexibility (ReFLEX) project [ref: 104780]. Valentin Robu was supported by the project “TESTBED2: Testing and Evaluating Sophisticated information and communication Technologies for enaBling scalablE smart griD Deployment”, funded by the European Union Horizon2020 Marie Skłodowska-Curie Actions ( MSCA) [Grant agreement number: 872172]. Ian Scott was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project - UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS. The authors would like to apologise for any inconvenience caused.publishersversionpublishe

Updated Typical Daily Load Profiles for LV Distribution Networks Customers

The evolution of power systems, especially at the distribution level for the increasing diffusion of new energy sources and new loads, makes urgent the need of updated models of the electrical behavior of LV customers. For this purpose, time series of consumption and generation have to be properly modeled to represent for instance, among the other goals, the impact of demand coincidence and of generation-load homotheticity. Despite the increasing diffusion of Smart Meters that could be used for producing more accurate and updated models, the current load profiling practices, neglect the actual variability among consumption habits of customers belonging to the same category. This paper proposes a method for clustering load profiles of LV customers, derived from two recent measurement campaigns in Italy, with the aim of finding updated typical load profile curves for different category of LV customers can be used in planning and operation studies. The comparison of the results obtained by applying the method to two available databases has been discussed

LV Customers Modeling Impact on Microgrid Optimal Management

Microgrid (MG) or Local Energy Communities (LEC) management systems aim at coordinating their local resources for minimizing the operation costs of their network. Excessive voltage reductions due to heavy demand or over voltage conditions due to production that exceed the local demand can be solved by exploiting control actions as the load shedding or the generation curtailment. The cost of these services, offered by players connected to the MG/LEC, have to be included in the total operational MG/LEC cost. Forecasting in advance, i.e. one day ahead, the state of the network, and the possible contingencies that may happen during the real time, can result in significant savings for the MG/LEC management system, because it is generally assumed that these services are more expensive in the real time than if purchased/planned in advance. Thus, one of the requirements of an optimal MG/LEC management system is to accurately model the local production and demand for making proper decisions in advance, that at least may be slightly changed if the forecasting does not happen in real time. Typical day load profiles that reproduce, in the best possible way, the behavior of the customers can be used for making this task more accurate. This paper compares the impact of using different sets of typical load profiles on the optimization performed by an Energy Management System (EMS), that controls the local MG/LEC resources for solving contingencies. The proposed case study is constituted by a LV MG/ LEC, derived from a real network and it is managed by an EMS based on a multi agent system

Emerging Business Models in Local Energy Markets: A Systematic Review of Peer-To-Peer, Community Self-Consumption, and Transactive Energy Models

The emergence of peer-to-peer, collective or community self-consumption, and transactive energy concepts gives rise to new configurations of business models for local energy trading among a variety of actors. Much attention has been paid in the academic literature to the transition of the underlying energy system with its macroeconomic market framework. However, fewer contributions focus on the microeconomic aspects of the broad set of involved actors. Even though specific case studies highlight single business models, a comprehensive analysis of emerging business models for the entire set of actors is missing. Following this research gap, this paper conducts a systematic literature review of 135 peer-reviewed journal articles to examine business models of actors operating in local energy markets. From 221 businesses in the reviewed literature, nine macro-actor categories are identified. For each type of market actor, a business model archetype is determined and characterised using the business model canvas. The key elements of each business model archetype are discussed, and areas are highlighted where further research is needed. Finally, this paper outlines the differences of business models for their presence in the three local energy market models. Focusing on the identified customers and partner relationships, this study highlights the key actors per market model and the character of the interactions between market participants

Emerging business models in local energy markets: A systematic review of peer-to-peer, community self-consumption, and transactive energy models

The emergence of peer-to-peer, collective or community self-consumption, and transactive energy concepts gives rise to new configurations of business models for local energy trading among a variety of actors. Much attention has been paid in the academic literature to the transition of the underlying energy system with its macroeconomic market framework. However, fewer contributions focus on the microeconomic aspects of the broad set of involved actors. Even though specific case studies highlight single business models, a comprehensive analysis of emerging business models for the entire set of actors is missing. Following this research gap, this paper conducts a systematic literature review of 135 peer-reviewed journal articles to examine business models of actors operating in local energy markets. From 221 businesses in the reviewed literature, nine macro-actor categories are identified. For each type of market actor, a business model archetype is determined and characterised using the business model canvas. The key elements of each business model archetype are discussed, and areas are highlighted where further research is needed. Finally, this paper outlines the differences of business models for their presence in the three local energy market models. Focusing on the identified customers and partner relationships, this study highlights the key actors per market model and the character of the interactions between market participants