Competitive Energy Trading Framework for Demand-Side Management in Neighborhood Area Networks

Abstract

This paper, by comparing three potential energy trading systems, studies the feasibility of integrating a community energy storage (CES) device with consumer-owned photovoltaic (PV) systems for demand-side management of a residential neighborhood area network. We consider a fully-competitive CES operator in a non-cooperative Stackelberg game, a benevolent CES operator that has socially favorable regulations with competitive users, and a centralized cooperative CES operator that minimizes the total community energy cost. The former two game-theoretic systems consider that the CES operator first maximizes their revenue by setting a price signal and trading energy with the grid. Then the users with PV panels play a non-cooperative repeated game following the actions of the CES operator to trade energy with the CES device and the grid to minimize energy costs. The centralized CES operator cooperates with the users to minimize the total community energy cost without appropriate incentives. The non-cooperative Stackelberg game with the fully-competitive CES operator has a unique Stackelberg equilibrium at which the CES operator maximizes revenue and users obtain unique Pareto-optimal Nash equilibrium CES energy trading strategies. Extensive simulations show that the fully-competitive CES model gives the best trade-off of operating environment between the CES operator and the users.Index Terms-Community energy storage, demand-side management, game theory, neighborhood area network.

An integrated optimization + learning approach to optimal dynamic pricing for the retailer with multi-type customers in smart grids

“…In general, algorithms to solve bilevel problems can be approximated categorized into three groups: iterative algorithms based on the definitions of bilevel/Stackelberg equilibriums (e.g., [27]), KKT based classical mathematical optimization algorithms (i.e. KKT based single level reduction) and GA type of metaheuristic algorithms [38].…”

Residential Energy Storage Management With Bidirectional Energy Control

“…Proof: To ensure (3) is satisfied, we must show the optimal control solution (26)-(30) in Proposition 1 can ensure (3) being satisfied. For Cases 1, 2 and 4, from their optimal control solutions (26), (27) and (30), it is easy to see that (3) is satisfied. For Cases 3 and 5, from their optimal control solutions (31) or (32) and (28) or (29), if F w d,t = W t − S w,t < D max , E w t = 0 and F s,t ≥ 0; If F w d,t = D max , we have F w s,t = 0 and E w t ≥ 0.…”

“…The idea of energy selling back or trading is considered in [26]- [28], where [26], [27] focus on demand-side management via pricing schemes using game approaches for load scheduling among customers, and [28] considers a microgrid operation and supply. In addition, although not explicitly modeled, the system considered in [24] can be generalized to include energy selling under a simplified model, provided that buying and selling prices are constrained such that the overall cost function is still convex.…”

Achieving Cost Minimization and Fairness in Multi-Supplier Smart Grid Environment

“…In the proposed system each supplier submits its bid to the DSM center, in response to this, the DSM center sets the price of electricity that is based on all the submitted bids. The work in [38] focused on a community energy storage (CES)-based SG where all the users have accurate forecast of their next day's power generation and energy requirement. This forecast is shared with the CES by each user.…”