Market Mechanisms for Local Electricity Markets: A review of models, solution concepts and algorithmic techniques

The rapidly increasing penetration of distributed energy resources (DERs) calls for a hierarchical framework where aggregating entities handle the energy management decisions of small DERs and represent these DERs upstream. These energy management decisions are typically envisaged to be made via market-based frameworks, aspiring the so-called Local Electricity Markets (LEMs). A rich literature of studies models such LEMs adopting various modeling assumptions and proposes various Market Mechanisms towards making dispatch and pricing decisions. In this paper, we make a systematic presentation of a LEM formulation, elaborating on the cornerstone attributes of the market model, i.e. the Market Scope, the Modeling Assumptions, the Market Objective, and the Market Mechanism. We discuss the different market model choices and their implications and then focus on the prevailing approaches of Market Mechanisms. Finally, we classify the relevant literature based on the market model that it adopts and the proposed Market Mechanism, visualize the results and also discuss patterns and trends

Fair and Scalable Electric Vehicle Charging Under Electrical Grid Constraints

The increasing penetration of electric vehicles brings a consequent increase in charging facilities in the low-voltage electricity network. Serving all charging requests on-demand can endanger the safety of the electrical power distribution network. This creates the issue of fairly allocating the charging energy among electric vehicles while maintaining the system within safe operational margins. However, calculating efficient charging schedules for the charging stations bears a high computational burden due to the non-convexities of charging stations' models. In this paper, we consider a tri-level system with electric vehicles, charging stations, and a power distribution system operator. The objective of each station is formulated as a max-min fairness, mixed-integer linear optimization problem, while the network constraints are modeled using a second-order conic formulation. In order to tackle the computational complexity of the problem, we decompose it and use a novel approximation method tailored to this problem. We compare the performance of the proposed method with that of the popular alternating direction method of multipliers. Our simulation results indicate that the proposed method achieves a near-optimal solution along with promising scalability properties.</p

Fair and Scalable Electric Vehicle Charging Under Electrical Grid Constraints

The increasing penetration of electric vehicles brings a consequent increase in charging facilities in the low-voltage electricity network. Serving all charging requests on-demand can endanger the safety of the electrical power distribution network. This creates the issue of fairly allocating the charging energy among electric vehicles while maintaining the system within safe operational margins. However, calculating efficient charging schedules for the charging stations bears a high computational burden due to the non-convexities of charging stations’ models. In this paper, we consider a tri-level system with electric vehicles, charging stations, and a power distribution system operator. The objective of each station is formulated as a max-min fairness, mixed-integer linear optimization problem, while the network constraints are modeled using a second-order conic formulation. In order to tackle the computational complexity of the problem, we decompose it and use a novel approximation method tailored to this problem. We compare the performance of the proposed method with that of the popular alternating direction method of multipliers. Our simulation results indicate that the proposed method achieves a near-optimal solution along with promising scalability properties

Mechanism Design for Fair and Efficient DSO Flexibility Markets

The proliferation of distributed energy assets necessitates the provision of flexibility to efficiently operate modern distribution systems. In this paper, we propose a flexibility market through which the DSO may acquire flexibility services from asset aggregators in order to maintain network voltages and currents within safe limits. A max-min fair formulation is proposed for the allocation of flexibility. Since the DSO is not aware of each aggregator&#x2019;s local flexibility costs, we show that strategic misreporting can lead to severe loss of efficiency. Using mechanism design theory, we provide a mechanism that makes it a payoff-maximizing strategy for each aggregator to make truthful bids to the flexibility market. While typical truthful mechanisms only work when the objective is the maximization of Social Welfare, the proposed mechanism lets the DSO achieve incentive compatibility and optimality for the the max-min fairness objective

Fair Congestion Management in Distribution Systems using Virtual Power Lines

Virtual power lines (VPLs) use utility-scale energy storage systems (ESS), acting as an alternative to reinforcing or building new infrastructure in congested transmission or distribution systems. This paper proposes a mathematical programming model for the congestion management of distribution systems by VPLs' optimal operation considering voltage and current magnitude operational limits. The model considers ESS and the optimal operation of lines' switches to minimize the energy curtailment among system nodes. The proposed formulation considers demand response and curtailment in photovoltaic (PV) generation, while facilitating the nodes' collective participation by adopting a Rawlsian social choice function. The model is cast as a mixed-integer second-order cone programming problem and is tested on a radial 34-node test system. Results showed a reduction of the curtailed energy from loads of more than 40% and from PVs of more than 75% using VPLs, while the fairness of the decisions was evaluated using Jain's index

Optimal Operation of Community Energy Storage using Stochastic Gradient Boosting Trees

This paper proposes an algorithm for the optimal operation of community energy storage systems (ESSs) using a machine learning (ML) model by solving a nonlinear programming (NLP) problem iteratively to obtain synthetic data. The NLP model minimizes the network's total energy losses by setting the community ESS's operation points. The optimization model is solved recursively by Monte Carlo simulations in a distribution system with high PV penetration, considering uncertainty in exogenous parameters. Obtained optimal solutions provide the training dataset for a stochastic gradient boosting trees (SGBT) ML algorithm following an imitation learning approach. The predictions obtained from the ML model have been compared to the optimal ESS operation to assess the model's accuracy. Furthermore, the ML model's sensitivity has been tested considering the sampling size and the number of predictors. Results showed a 98% of accuracy for the SGBT model compared to optimal solutions. This accuracy was obtained even after a reduction of 83% in the number of predictors