Optimization-based modelling and game-theoretic framework for techno-economic analysis of demand-side flexibility: a real case study

This paper proposes a two-step framework for techno-economic analysis of a demand-side flexibility service in distribution networks. Step one applies optimization-based modelling to propose a generic problem formulation which determines the offer curve, in terms of available flexible capacity and its marginal cost, for flexible distribution-connected assets. These offer curves form an input to the second step, which uses a multi-agent iterative game framework to determine the benefits of demand-side flexibility for the Distribution System Operator (DSO) and the service providers. The combined two-step framework simultaneously accounts for the objectives of each flexibility provider, technical constraints of flexible assets, customer preferences, market clearing mechanisms, and strategic bidding by service providers, omission of any of which can lead to erroneous results. The proposed two-step framework has been applied to a real case study in the North East of England to examine four market mechanisms and three bidding strategies. The results showed that among all considered market mechanisms, flexibility markets that operate under discriminatory pricing, such as pay-as-bid and Dutch reverse auctions, are prone to manipulations, especially in the lack of competition. In contrast, uniform pricing pay-as-cleared auction provides limited opportunities for manipulation even when competition is low.

[1]  A. Wambach,et al.  Bid pooling in reverse multi-unit Dutch auctions: an experimental investigation , 2016 .

[2]  N. Brandon,et al.  Role and value of flexibility in facilitating cost-effective energy system decarbonisation , 2020, Progress in Energy.

[3]  Fotis D. Kanellos,et al.  Power Management Method for Large Ports With Multi-Agent Systems , 2019, IEEE Transactions on Smart Grid.

[4]  Masood Parvania,et al.  Optimal Participation of Water Desalination Plants in Electricity Demand Response and Regulation Markets , 2020, IEEE Systems Journal.

[5]  Mazhar Ali,et al.  Alternating direction method of multipliers for the optimal siting, sizing, and technology selection of Li-ion battery storage , 2020 .

[6]  Emmanouel Varvarigos,et al.  Designing a Distribution Level Flexibility Market using Mechanism Design and Optimal Power Flow , 2020, 2020 International Conference on Smart Energy Systems and Technologies (SEST).

[7]  Nikolaos G. Paterakis,et al.  Transactive Energy for Flexible Prosumers Using Algorithmic Game Theory , 2021, IEEE Transactions on Sustainable Energy.

[8]  Inês F. G. Reis,et al.  A multi-agent system approach to exploit demand-side flexibility in an energy community , 2020 .

[9]  Behnam Mohammadi-Ivatloo,et al.  Robust bidding strategy for demand response aggregators in electricity market based on game theory , 2020 .

[10]  Birgitte Bak-Jensen,et al.  A multi-agent based optimization of residential and industrial demand response aggregators , 2019, International Journal of Electrical Power & Energy Systems.

[11]  Masoud Rashidinejad,et al.  Risk-Constrained Bidding Strategy for Demand Response, Green Energy Resources, and Plug-In Electric Vehicle in a Flexible Smart Grid , 2021, IEEE Systems Journal.

[12]  Danna Zhou,et al.  d. , 1840, Microbial pathogenesis.

[13]  K. Poplavskaya,et al.  Effect of market design on strategic bidding behavior: Model-based analysis of European electricity balancing markets , 2020, Applied Energy.

[14]  T. Sayfutdinov,et al.  Optimal Utilization Strategy of the LiFePO4 Battery Storage , 2021, ArXiv.

[15]  Graeme Hawker,et al.  Assessing domestic heat storage requirements for energy flexibility over varying timescales , 2018 .

[16]  M. Rahimiyan,et al.  Strategic Bidding for a Virtual Power Plant in the Day-Ahead and Real-Time Markets: A Price-Taker Robust Optimization Approach , 2016, IEEE Transactions on Power Systems.

[17]  Vincent W. S. Wong,et al.  Advanced Demand Side Management for the Future Smart Grid Using Mechanism Design , 2012, IEEE Transactions on Smart Grid.

[18]  A.G. Bakirtzis,et al.  Agent-Based Simulation of Power Markets under Uniform and Pay-as-Bid Pricing Rules using Reinforcement Learning , 2006, 2006 IEEE PES Power Systems Conference and Exposition.

[19]  William D'haeseleer,et al.  Integrating Electric Vehicles as Flexible Load in Unit Commitment Modeling , 2014 .

[20]  Danny Pudjianto,et al.  Quantifying the Potential Economic Benefits of Flexible Industrial Demand in the European Power System , 2018, IEEE Transactions on Industrial Informatics.

[21]  P. Alam ‘W’ , 2021, Composites Engineering.

[22]  Michael H. Rothkopf,et al.  Thirteen Reasons Why the Vickrey-Clarke-Groves Process Is Not Practical , 2007, Oper. Res..

[23]  P. Alam ‘N’ , 2021, Composites Engineering: An A–Z Guide.

[24]  P. Alam ‘E’ , 2021, Composites Engineering: An A–Z Guide.

[25]  Raman Paranjape,et al.  Multi-Agent Optimization for Residential Demand Response under Real-Time Pricing , 2019 .

[26]  Emiliano Dall'Anese,et al.  Aggregate Power Flexibility in Unbalanced Distribution Systems , 2018, IEEE Transactions on Smart Grid.

[28]  Danny H. K. Tsang,et al.  Aggregation of Demand-Side Flexibility in Electricity Markets: Negative Impact Analysis and Mitigation Method , 2021, IEEE Transactions on Smart Grid.

[29]  Munther A. Dahleh,et al.  Data-driven control of micro-climate in buildings; an event-triggered reinforcement learning approach , 2020, Applied Energy.

[30]  Nikolaos G. Paterakis,et al.  Mechanism Design for Fair and Efficient DSO Flexibility Markets , 2021, IEEE Transactions on Smart Grid.