A Consensus-ADMM Approach for Strategic Generation Investment in Electricity Markets

This paper addresses a multi-stage generation investment problem for a strategic (price-maker) power producer in electricity markets. This problem is exposed to different sources of uncertainty, including short-term operational (e.g., rivals' offering strategies) and long-term macro (e.g., demand growth) uncertainties. This problem is formulated as a stochastic bilevel optimization problem, which eventually recasts as a large-scale stochastic mixed-integer linear programming (MILP) problem with limited computational tractability. To cope with computational issues, we propose a consensus version of alternating direction method of multipliers (ADMM), which decomposes the original problem by both short- and long-term scenarios. Although the convergence of ADMM to the global solution cannot be generally guaranteed for MILP problems, we introduce two bounds on the optimal solution, allowing for the evaluation of the solution quality over iterations. Our numerical findings show that there is a trade-off between computational time and solution quality

Centralised and Distributed Optimization for Aggregated Flexibility Services Provision

The recent deployment of distributed battery units in prosumer premises offer new opportunities for providing aggregated flexibility services to both distribution system operators and balance responsible parties. The optimization problem presented in this paper is formulated with an objective of cost minimization which includes energy and battery degradation cost to provide flexibility services. A decomposed solution approach with the alternating direction method of multipliers (ADMM) is used instead of commonly adopted centralised optimization to reduce the computational burden and time, and then reduce scalability limitations. In this work we apply a modified version of ADMM that includes two new features with respect to the original algorithm: first, the primal variables are updated concurrently, which reduces significantly the computational cost when we have a large number of involved prosumers; second, it includes a regularization term named Proximal Jacobian (PJ) that ensures the stability of the solution. A case study is presented for optimal battery operation of 100 prosumer sites with real-life data. The proposed method finds a solution which is equivalent to the centralised optimization problem and is computed between 5 and 12 times faster. Thus, aggregators or large-scale energy communities can use this scalable algorithm to provide flexibility services.Comment: 10 pages, 7 figure

Scheduling of Resources in Renewable Energy Communities

This work presents a detailed study of the scheduling of power and energy resources in renewable energy communities (RECs). The study has been developed starting from the analysis of a single basic unit of the community, i.e., the prosumer and its microgrid, to the scheduling and expansion of the energy community concept with several prosumers through several scenarios. The individual scheduling problem of the prosumer has been studied as a day-ahead deterministic problem and as a multistage stochastic problem to consider uncertainties associated with energy generation and energy consumption. Furthermore, an approach has been formulated to consider the integration of bidirectional charging services of electrical vehicles within a local energy system with the presence of renewable generation. Moreover, this thesis focuses on the scenario in which direct energy transactions between prosumers located within a REC are allowed in addition to the energy transactions with the external energy provider. The day-ahead scheduling problem has been addressed by a centralized approach and by a distributed approach based on the alternating direction method of multipliers (ADMM). The developed approaches provide the scheduling of the available energy resources to limit the balancing action of the external grid and allocate the internal network losses to the corresponding energy transactions. Finally, the thesis presents a coordinated day-ahead and intra-day approach to provide the optimal scheduling of the resources in a REC. In this case, the ADMM-based procedure, which is aimed at minimizing the total energy procurement costs, is adapted to cope with the impact of the fluctuation of both the local energy generation and demand during the day. To achieve this, a day-ahead multistage stochastic optimization approach is combined with an intra-day decision-making procedure, able to adjust the scheduling of the energy resources according to the current operational conditions