Flexibility Aggregation of Temporally Coupled Resources in Real Time Balancing Markets Using Machine Learning

In modern power systems with high penetration of renewable energy sources, the flexibility provided by distributed energy resources is becoming invaluable. Demand aggregators offer balancing energy in the real-time balancing market on behalf of flexible resources. A challenging task is the design of the offering strategy of an aggregator. In particular, it is difficult to capture the flexibility cost of a portfolio of flexibility assets within a price-quantity offer, since the costs and constraints of flexibility resources exhibit inter-temporal dependencies. In this article, we propose a generic method for constructing aggregated balancing energy offers that best represent the portfolio's actual flexibility costs, while accounting for uncertainty in future timeslots. For the case study presented, we use offline simulations to train and compare different machine learning (ML) algorithms that receive the information about the state of the flexible resources and calculate the aggregator's offer. Once trained, the ML algorithms can make fast decisions about the portfolio's balancing energy offer in the real-time balancing market. Our simulations show that the proposed method performs reliably towards capturing the flexibility of the Aggregator's portfolio and minimizing the aggregator's imbalances.</p

Transactive Energy for Flexible Prosumers Using Algorithmic Game Theory

In modern smart grids, the focus is increasingly shifted towards distributed energy resources and flexible electricity assets owned by prosumers. A system with high penetration of flexible prosumers, has a very large number of variables and constraints, while a lot of the information is local and non-observable. Decomposition methods and local problem solving is considered a promising approach for such settings, particularly when the implementation of a decomposition method features a market-based analogy, i.e. it can be implemented in a Transactive Energy fashion. In this paper we present an auction-theoretic scheme for a setting with non-convex prosumer models and resource constraints. The scheme is evaluated on a particular case study and its scalability and efficiency properties are tested and compared to an optimal benchmark solution. A game-theoretic analysis is made with respect to how an intelligent agent, that bids on behalf of a prosumer can try to strategize within the auction, in order to make itself better-off. Our simulations show that there is an alignment of incentives, i.e., when the prosumers try to strategize, they actually improve the auction's efficiency

Max-min fairness for demand side management under high RES penetration:Dealing with undefined consumer valuation functions

In energy communities with high penetration of renewables, electricity supply can become scarce during certain periods. In such cases, the objective of a resource allocation algorithm can be to minimize the disutility of the worst-off user of the community. This paper presents the problem of finding an optimal max-min fair allocation of available energy. A mixed-integer linear formulation is used to tackle the problem, so that the worst-off user’s disutility is minimized. The allocation results are compared to the standard approach that optimizes the system’s Social Welfare. The users’ disutility function models for electricity consuming devices, are based on their time-under-unsatisfied-demand, which is a measurable and comparable metric and does not rely on the user’s self reported valuation for energy consumption

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

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

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

A Tariff Structure for Reliability of Power Supply Levels in Congested Low Voltage Networks

Modern low voltage networks must accommodate the increase in adoption of new forms of electricity consumption, such as electric vehicles and heat pumps, induced by the energy transition. With the current grid policies and tariffs structures, large investments will be required by Distribution System Operators to increase the hosting capacity of the network. New tariff structures that channel the inherent flexibility of the new assets being adopted can help mitigate future congestion problems and maximize the use of existing infrastructure. In this paper, a mechanism to allocate transport capacity in low voltage networks is presented. The allocation mechanism is based on the notion of non-firm transport capacity, that is, network capacity that is not guaranteed 24/7 to the customer. With the proposed mechanism, users are given the option of contracting parts of their grid connection with varying levels of reliability. The user demand for reliability is modeled with utility functions. The allocation mechanism is evaluated on a particular case study, where electric vehicle charging increases network congestion. The results show that the application of such a mechanism can help alleviate congestion in a network via the safe allocation (within the technical limits) of transport capacity

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

Managing Distributed Flexibility under Uncertainty by Combining Deep Learning with Duality

In modern power systems, small distributed energy resources (DERs) are considered a valuable source of flexibility towards accommodating high penetration of Renewable Energy Sources (RES). In this paper we consider an economic dispatch problem for a community of DERs, where energy management decisions are made online and under uncertainty. We model multiple sources of uncertainty such as RES, wholesale electricity prices as well as the arrival times and energy needs of a set of Electric Vehicles. The economic dispatch problem is formulated as a multi-agent Markov Decision Process. The difficulties lie in the curse of dimensionality and in guaranteeing the satisfaction of constraints under uncertainty. A novel method, that combines duality theory and deep learning, is proposed to tackle these challenges. In particular, a Neural Network (NN) is trained to return the optimal dual variables of the economic dispatch problem. By training the NN on the dual problem instead of the primal, the number of output neurons is dramatically reduced, which enhances the performance and reliability of the NN. Finally, by treating the resulting dual variables as prices, each distributed agent can self-schedule, which guarantees the satisfaction of its constraints. As a result, our simulations show that the proposed scheme performs reliably and efficiently

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