Improving the Scalability of a Prosumer Cooperative Game with K-Means Clustering

Among the various market structures under peer-to-peer energy sharing, one model based on cooperative game theory provides clear incentives for prosumers to collaboratively schedule their energy resources. The computational complexity of this model, however, increases exponentially with the number of participants. To address this issue, this paper proposes the application of K-means clustering to the energy profiles following the grand coalition optimization. The cooperative model is run with the "clustered players" to compute their payoff allocations, which are then further distributed among the prosumers within each cluster. Case studies show that the proposed method can significantly improve the scalability of the cooperative scheme while maintaining a high level of financial incentives for the prosumers.Comment: 6 pages, 4 figures, 2 tables. Accepted to the 13th IEEE PES PowerTech Conference, 23-27 June 2019, Milano, Ital

Stochastic Hosting Capacity in LV Distribution Networks

Hosting capacity is defined as the level of penetration that a particular technology can connect to a distribution network without causing power quality problems. In this work, we study the impact of solar photovoltaics (PV) on voltage rise. In most cases, the locations and sizes of the PV are not known in advance, so hosting capacity must be considered as a random variable. Most hosting capacity methods study the problem considering a large number of scenarios, many of which provide little additional information. We overcome this problem by studying only cases where voltage constraints are active, with results illustrating a reduction in the number of scenarios required by an order of magnitude. A linear power flow model is utilised for this task, showing excellent performance. The hosting capacity is finally studied as a function of the number of generators connected, demonstrating that assumptions about the penetration level will have a large impact on the conclusions drawn for a given network

Annealing-based Quantum Computing for Combinatorial Optimal Power Flow

This paper proposes the use of annealing-based quantum computing for solving combinatorial optimal power flow problems. Quantum annealers provide a physical computing platform which utilises quantum phase transitions to solve specific classes of combinatorial problems. These devices have seen rapid increases in scale and performance, and are now approaching the point where they could be valuable for industrial applications. This paper shows how an optimal power flow problem incorporating linear multiphase network modelling, discrete sources of energy flexibility, renewable generation placement/sizing and network upgrade decisions can be formulated as a quadratic unconstrained binary optimisation problem, which can be solved by quantum annealing. Case studies with these components integrated with the IEEE European Low Voltage Test Feeder are implemented using D-Wave Systems' 5,760 qubit Advantage quantum processing unit and hybrid quantum-classical solver

Annealing-based quantum computing for combinatorial optimal power flow

This paper proposes the use of annealing-based quantum computing for solving combinatorial optimal power flow problems. Quantum annealers provide a physical computing platform which utilises quantum phase transitions to solve specific classes of combinatorial problems. These devices have seen rapid increases in scale and performance, and are now approaching the point where they could be valuable for industrial applications. This paper shows how an optimal power flow problem incorporating linear multiphase network modelling, discrete sources of energy flexibility, renewable generation placement/sizing and network upgrade decisions can be formulated as a quadratic unconstrained binary optimisation problem, which can be solved by quantum annealing. Case studies with these components integrated with the IEEE European Low Voltage Test Feeder are implemented using D-Wave Systems' 5,760 qubit Advantage quantum processing unit and hybrid quantum-classical solver

Call-options in Peer-to-Peer Energy Markets

This paper proposes the novel application of call-options for financial loss mitigation in a peer-to-peer (P2P) energy market. P2P energy markets present the opportunity for end-users to trade electricity among themselves by managing their electricity usage and production capabilities. But variability characteristics of renewable resources pose a fundamental challenge to their integration into the grid as well as participating in emerging P2P energy markets. The growing penetration of renewable supply will increase the need for tools to mitigate potential energy traders' financial losses. This paper proposes and evaluates the application of call-option contracts in P2P markets to hedge against financial losses related to power shortfall in renewable supply. A case study is presented, showing that P2P traders might have to bear financial losses when they cannot meet their market obligations, and how options can be used to mitigate such losses

The energy flexibility divide: an analysis of whether energy flexibility could help reduce deprivation in Great Britain

The provision of energy flexibility services (such as shifting consumption) to electricity systems is becoming increasingly valuable, and can offer additional income for households. Here, we show how the locational distribution of flexibility impacts its value, and whether this could help reduce deprivation in Great Britain. Geospatial analysis shows that nearly 90 % of people (1.3 million) living in the most deprived areas of Greater London can offer high-value flexibility. This could help improve their economic condition, provided that the adoption of appropriate appliances (such as demand response devices) is incentivised, e.g. through government's spatially targeted incentive schemes. The results show that the provision of flexibility could help reduce deprivation in several regions, including Scotland, Greater London, and Yorkshire. By contrast, other areas such as North and North-East England tend to offer lower-value flexibility, and therefore the benefit would be smaller. A flexibility-adjusted deprivation index is proposed to highlight regions where providing flexibility may most help reduce deprivation

OPLEM: Open Platform for Local Energy Markets

Local Energy Markets (LEMs) have been proposed as an effective solution to coordinate distributed energy resources within emerging smart local energy systems. However, LEMs are still at an early stage of development and are not widely implemented yet. Extensible open-source software tools are needed to simulate different market designs and evaluate their feasibility and effectiveness. This paper presents OPLEM, an open-source Python package for modelling and testing LEM designs. It offers a modular and flexible framework to create and test market designs adapted for distribution networks. OPLEM integrates two distinct models—one for designing and simulating local energy markets, and another for network operation. Unlike existing tools, OPLEM platform offers a comprehensive solution that bridges the gap between market design and network operation assessment, enabling stakeholders to gain deeper insights into the interactions between market dynamics and grid performance. Additionally, the inclusion of participant and asset models further enhances the tool's versatility, allowing for a holistic analysis. Thanks to its modular structure, OPLEM allows the development of customised LEM designs and initially incorporates four popular LEM designs: Time-of-Use pricing market, centralised dispatch with distribution locational marginal pricing, bilateral peer-to-peer market and auction market. By combining market design and network modelling functionalities, the tool empowers users to explore various scenarios and optimise market clearings to enhance both economic efficiency and grid reliability

Opportunities for quantum computing within net-zero power system optimization

Key conclusions: In this review, we identify significant and wide-ranging opportunities for recent breakthroughs in quantum-accelerated optimization to offer value for the transition to net-zero power systems. These opportunities span a variety of problems across planning and operation, which are key for reliable and affordable decarbonization. Seminal discoveries highlighted in the review: We review the latest work on quantum computing for combinatorial power system optimization applications, including unit commitment, grid-edge flexibility coordination, and network expansion planning. In addition, we map state-of-the-art theoretical work to applications where quantum computing is underexplored, including convex and machine learning-based optimization. Implications for research at different scales: Quantum computing creates opportunities for faster, larger-scale, and higher-fidelity optimization. This is relevant for researchers from engineering, economics, and computer science, as well as policymakers, network planners, system operators, and flexibility aggregators. Potential future directions: To address challenges for industry implementation and scale-up, we propose new research into (1) benchmark problem definitions and performance criteria; (2) domain-specific algorithms and hardware for current noisy intermediate-scale devices; and (3) holistic power industry computing strategies integrating quantum computing with more immediate areas of classical computing innovation