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We consider the stochastic dual dynamic programming (SDDP) algorithm - a widely employed algorithm applied to multistage stochastic programming - and propose a variant using experience replay - a batch learning technique from reinforcement learning. To connect SDDP with reinforcement learning, we cast SDDP as a Q-learning algorithm and describe its application in both risk-neutral and risk-averse settings. We demonstrate the superiority of the algorithm over conventional SDDP by benchmarking it against PSR's SDDP software using a large-scale instance of the long-term planning problem of inter-connected hydropower plants in Colombia. We find that SDDP with batch learning is able to produce tighter optimality gaps in a shorter amount of time than conventional SDDP. We also find that batch learning improves the parallel efficiency of SDDP backward passes.

We propose a chance-constrained formulation for the problem of dimensioning frequency restoration reserves on a power transmission network. We cast our problem as a two-stage stochastic mixed integer linear program, and propose a heuristic algorithm for solving the problem. Our model accounts for the simultaneous sizing of both upward and downward reserves, and uncertainty driven by imbalances, contingencies and available transmission capacity. Our core methodology is further adapted in order to minimize inter-zonal flows and in order to split reserve requirements between automatic and manual frequency restoration reserves. We apply our methodology to the problem of sizing reserves in the four load frequency control areas of the Swedish power system. We demonstrate the benefits of our method in terms of decreasing reserve requirements in the absence of reserve sharing, we analyze the spatial allocation of reserves, and we perform various sensitivity analyses.

Convex hull pricing is a well-documented method for coping with the non-existence of uniform clearing prices in electricity markets with non-convex costs and constraints. We revisit primal and dual methods for computing convex hull prices, and discuss the positioning of existing approximation methods in this taxonomy. We propose a dual decomposition algorithm known as the Level Method and we adapt the basic algorithm to the specificities of convex hull pricing. We benchmark its performance against a column generation algorithm that has recently been proposed in the literature. We provide empirical evidence about the favorable performance of our algorithm on large test instances based on PJM and Central Europe.

Scarcity pricing has been proposed to enhance investment in flexible assets through the use of an adder on real-time energy and the application of that adder on real-time reserve. We implement a Monte-Carlo simulator for obtaining statistically confident estimates of scarcity pricing adders which is motivated from the implementation of this mechanism in Belgium. The analysis is based on a multi-level, multi-horizon simulation of day-ahead and real-time operations in the Belgian market. The methodology relies on k-means clustering for selecting a set of representative day-ahead forecasts, followed by the generation of synthetic real-time load scenarios for simulating real-time operations.

In an era of energy transition, it is crucial to ensure that the design of the short-term electricity market provides sufficient cash flows to producers so as to allow the investment of the right technology at the right location. In this paper, we revisit the question of capacity allocation in zonal markets from a long-term perspective. We model the capacity expansion problem in zonal markets in which inter-zonal transmission capacity allocation is organized through flow-based market coupling, which is an approximation of power flow equations in aggregate networks that is employed in European market design. We demonstrate that the classical result of equivalence between centralized and decentralized formulations in transmission-constrained markets ceases to hold in this case. We propose a model of the decentralized capacity expansion problem with flow-based market coupling as a generalized Nash equilibrium that we formulate as a linear complementarity problem. We then perform simulations of the capacity expansion problem with nodal pricing and three variations of zonal pricing on a realistic instance of the Central Western European network and comment on the impacts of flow-based market coupling on investment.

Priority service and multilevel demand subscription have been proposed as two alternative methods for the mobilization of residential demand response. Whereas priority service relies on the differentiation of electricity service according to reliability, multilevel demand subscription further differentiates electricity service according to duration. Despite its increased complexity, multilevel demand subscription promises increased operational efficiency, as it permits a finer differentiation of consumer classes by the utility. It also allows households to reduce their electricity bills relative to priority service. This paper proposes a framework for quantifying these effects. We design a modeling approach for evaluating the performance of these different aggregator service offerings in a system with utility-scale renewable supply, residential renewable supply, and residential storage. We compare priority service to multilevel demand subscription, and discuss the implications of these different residential demand response options on operational efficiency and consumer expenditures for electricity service on a realistic model of the Belgian power market. We show how the comparison between the two schemes is affected by the adoption of a different time resolution in a detailed case study.