A Profit-Neutral Double-price-signal Retail Electricity Market Solution for Incentivizing Price-responsive DERs Considering Network Constraints

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Virginia Tech


Emerging technologies, including distributed energy resources (DERs), internet-of-things and advanced distribution management systems, are revolutionizing the power industry. They provide benefits like higher operation flexibility and lower bulk grid dependency, and are moving the modern power grid towards a decentralized, interconnected and intelligent direction. Consequently, the emphasis of the system operation management has been shifted from the supply-side to the demand-side. It calls for a reconsideration of the business model for future retail market operators. To address this need, this dissertation proposes an innovative retail market solution tailored to market environments penetrated with price-responsive DERs. The work is presented from aspects of theoretical study, test-bed platform development, and experimental analysis, within which two topics relevant to the retail market operation are investigated in depth.

The first topic covers the modeling of key retail market participants. With regard to price-insensitive participants, fixed loads are treated as the representative. Deep learning-based day-ahead load forecasting models are developed in this study, utilizing both recurrent and convolutional neural networks, to predict the part of demands that keep fixed regardless of the market price. With regard to price-sensitive participants, battery storages are selected as the representative. An optimization-based battery arbitrage model is developed in this study to represent their price-responsive behaviors in response to a dynamic price. The second topic further investigates how the retail market model and pricing strategy should be designed to incentivize these market participants. Different from existing works, this study innovatively proposes a profit-neutral double-price-signal retail market model. Such a design differentiates elastic prosumers, who actively offer flexibilities to the system operation, from normal inelastic consumers/generators, based on their sensitivities to the market price. Two price signals, namely retail grid service price and retail energy price, are then introduced to separately quantify values of the flexibility, provided by elastic participants, and the electricity commodity, sold/bought to/from inelastic participants. Within the proposed retail market, a non-profit retail market operator (RMO) manages and settles the market through determining the price signals and supplementary subsidy to minimize the overall system cost. In response to the announced retail grid service price, elastic prosumers adjust their day-ahead operating schedules to maximize their payoffs. Given the interdependency between decisions made by the RMO and elastic participants, a retail pricing scheme, formulated based on a bi-level optimization framework, is proposed. Additional efforts are made on merging and linearizing the original non-convex bi-level problem into a single-level mixed-integer linear programming problem to ensure the computational efficiency of the retail pricing tool.

Case studies are conducted on a modified IEEE 34-bus test-bed system, simulating both physical operations of the power grid and financial interactions inside the retail market. Experimental results demonstrate promising properties of the proposed retail market solution: First of all, it is able to provide cost-saving benefits to inelastic customers and create revenues for elastic customers at the same time, justifying the rationalities of these participants to join the market. Second of all, the addition of the grid service subsidy not only strengthens the profitability of the elastic customer, but also ensures that the benefit enjoyed per customer will not be compromised by the competition brought up by a growing number of participants. Furthermore, it is able to properly capture impacts from line losses and voltage constraints on the system efficiency and stability, so as to derive practical pricing solutions that respect the system operating rules. Last but not least, it encourages the technology improvement of elastic assets as elastic assets in better conditions are more profitable and could better save the electricity bills for inelastic customers. Above all, the superiority of the proposed retail market solution is proven. It can serve as a promising start for the retail electricity market reconstruction.



Retail Electricity Market, Load Forecasting, Battery Arbitrage, Bi-level Optimization, Deep learning (Machine learning)