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The effective pricing of electric vehicle (EV) charging by aggregators constitutes a key problem toward the realization of the significant EV flexibility potential in deregulated electricity systems and has been addressed by previous work through bi-level optimization formulations. However, the solution approach adopted in previous work cannot capture the discrete nature of the EV charging/discharging levels. Although reinforcement learning (RL) can tackle this challenge, state-of-the-art RL methods require discretization of state and/or action spaces and thus exhibit limitations in terms of solution optimality and computational requirements. This article proposes a novel deep reinforcement learning (DRL) method to solve the examined EV pricing problem, combining deep deterministic policy gradient (DDPG) principles with a prioritized experience replay (PER) strategy and setting up the problem in multi-dimensional continuous state and action spaces. Case studies demonstrate that the proposed method outperforms state-of-the-art RL methods in terms of both solution optimality and computational requirements and comprehensively analyze the economic impacts of smart-charging and vehicle-to-grid (V2G) flexibility on both aggregators and EV owners. [ABSTRACT FROM AUTHOR]

Bi-level optimization and reinforcement learning (RL) constitute the state-of-the-art frameworks for modeling strategic bidding decisions in deregulated electricity markets. However, the former neglects the market participants’ physical non-convex operating characteristics, while conventional RL methods require discretization of state and/or action spaces and thus suffer from the curse of dimensionality. This paper proposes a novel deep reinforcement learning (DRL) based methodology, combining a deep deterministic policy gradient (DDPG) method with a prioritized experience replay (PER) strategy. This approach sets up the problem in multi-dimensional continuous state and action spaces, enabling market participants to receive accurate feedback regarding the impact of their bidding decisions on the market clearing outcome, and devise more profitable bidding decisions by exploiting the entire action domain, also accounting for the effect of non-convex operating characteristics. Case studies demonstrate that the proposed methodology achieves a significantly higher profit than the alternative state-of-the-art methods, and exhibits a more favourable computational performance than benchmark RL methods due to the employment of the PER strategy. [ABSTRACT FROM AUTHOR]

Bi-level optimization constitutes the most popular mathematical methodology for modeling the deregulated electricity market. However, state-of-the-art models neglect the physical non-convex operating characteristics of market participants, due to their inherent inability to capture binary decision variables in their representation of the market clearing process, rendering them problematic in modeling markets with complex bidding and unit commitment (UC) clearing mechanisms. This paper addresses this fundamental limitation by proposing a novel modeling approach enabling incorporation of these non-convexities into bi-level optimization market models, which is based on the relaxation and primal-dual reformulation of the original, non-convex lower level problem and the penalization of the associated duality gap. Case studies demonstrate the ability of the proposed approach to closely approximate the market clearing solution of the actual UC clearing algorithm and devise more profitable bidding decisions for strategic producers than the state-of-the-art bi-level optimization approach, and reveal the potential of strategic behavior in terms of misreporting non-convex operating characteristics. [ABSTRACT FROM AUTHOR]

Previous work on the role of the demand side in imperfect electricity markets has demonstrated that its self-price elasticity reduces electricity producers’ ability to exercise market power. However, the concept of self-price elasticity cannot accurately capture consumers’ flexibility, as the latter mainly involves shifting of loads’ operation in time. This paper provides for the first time theoretical and quantitative analysis of the beneficial impact of demand shifting (DS) in mitigating market power by the generation side. Quantitative analysis is supported by a multiperiod equilibrium programming model of the imperfect electricity market, accounting for the time-coupling operational constraints of DS as well as network constraints. The decision making process of each strategic producer is modeled through a bi-level optimization problem, which is solved after converting it to a Mathematical Program with Equilibrium Constraints (MPEC) and linearizing the latter through suitable techniques. The oligopolistic market equilibria resulting from the interaction of multiple independent producers are determined by employing an iterative diagonalization method. Case studies on a test market reflecting the general generation and demand characteristics of the GB system quantitatively demonstrate the benefits of DS in mitigating producers’ market power, by employing relevant indexes from the literature. [ABSTRACT FROM AUTHOR]

• Local energy markets enable direct energy trading among participating customers. • The interactions among electricity retailers and local energy markets are modelled. • A novel approach is employed for solving the developed bi-level optimization model. • Local energy markets are shown to enhance the competitiveness of retailers. • Local energy markets are shown to increase the economic surplus of the customers. Local energy markets (LEM) have recently attracted great interest as they enable effective coordination of small-scale distributed energy resources (DER) at the customer side, and avoidance of distribution network reinforcements. However, the introduction of LEM has also significant implications on the strategic interactions between the customers and incumbent electricity retailers. This paper explores for the first time these interactions by proposing a novel multi-period bi-level optimization model, which captures the pricing decisions of a strategic retailer in the upper level (UL) and the response of both independent customers and the LEM (both including flexible consumers, micro-generators and energy storages) in the lower level (LL). Since the LL problem representing the LEM is non-convex, a new analytical approach is employed for solving the developed bi-level problem. The examined case studies demonstrate that the introduction of an LEM reduces the customers' energy dependency on the retailer and limits the retailer's strategic potential of exploiting the customers through large differentials between buy and sell prices. As a result, the profit of the retailer is significantly reduced while the customers, primarily the LEM participants and to a lower extent non-participating customers, achieve significant economic benefits. [ABSTRACT FROM AUTHOR]

• The long-term impacts of demand flexibility on deregulated systems are investigated. • Bi-level optimization model of a generation company's investment problem is proposed. • The proposed model captures energy shifting and reserve provision of flexible demand. • Different market design options around the allocation of reserve payments are tested. Demand flexibility has attracted significant interest given its potential to address techno-economic challenges associated with the decarbonisation of electricity systems. However, previous work has investigated its long-term impacts through centralized generation planning models which do not reflect the current deregulated environment. At the same time, existing market-based generation planning models are inherently unable to capture the demand flexibility potential since they neglect time-coupling effects and system reserve requirements in their representation of the electricity market. This paper investigates the long-term impacts of demand flexibility in the deregulated environment, by proposing a time-coupling, bi-level optimization model of a self-interested generation company's investment planning problem, which captures for the first time the energy shifting flexibility of the demand side and the operation of reserve markets with demand side participation. Case studies investigate different cases regarding the flexibility of the demand side and different market design options regarding the allocation of reserve payments. The obtained results demonstrate that, in contrast with previous centralised planning models, the proposed model can capture the dependency of generation investment decisions and the related impacts of demand flexibility on the electricity market design and the subsequent strategic response of the self-interested generation company. [ABSTRACT FROM AUTHOR]