Power system flexibility is seen as a key for addressing the management challenges in the future smart grids. Distributed Energy Resources (DERs) can provide the necessary flexibility to: a) maintain a stable frequency and a secure energy supply in an overall system perspective, and b) maintain bus voltages and secure transfer capacities in their local networks. In this context, Flexibility Service Providers (FSPs) facilitate the management of the transmission and the distribution network, while at the same time they optimally exploit their DERs. This work proposes a bilevel model for an FSP owning distributed Battery Storage Units and participating in: i) wholesale Energy, Reserve and Balancing Markets, and ii) a novel distribution-level Flexibility Market. The developed model is applied to the IEEE 33-bus radial distribution system and the results demonstrate that it achieves superlinear gains. Finally, a sensitivity analysis is conducted to study the impact of several externalities on the FSP's decisions.

As Distributed Energy Resources (DER) penetration levels and distributed flexibility investments are continuously growing, various smart grid actors need to coordinate their decisions towards optimal DER siting and sizing: First, profit-based Energy Service Providers (ESPs) want to secure their long-term profits and avoid economically unsustainable DER investments. Second, Distribution System Operators (DSOs) need to ensure the reliable operation of their networks in an economically optimal way. Third, Transmission System Operators (TSOs) want to optimally exploit the available “clean” DERs in close collaboration with the downstream DSOs. In this paper, we propose a novel ESP-DSO-TSO coordination scheme to co-optimize distributed renewable energy and storage planning at the distribution network level, while modeling the coordinated TSO-DSO operations. We formulate a bi-level program, the upper-level of which minimizes the DSO's costs, ensuring a minimum rate of return on ESP's investments, while the lower-level models the transmission network-constrained wholesale market. A nested decomposition technique is used to achieve computational tractability. Simulation results showcase a trade-off analysis between sustainable DER investments and system cost minimization and prove that an ESP-DSO-TSO interaction can benefit all involved actors to a certain extent. Finally, the computational efficiency of the proposed nested decomposition algorithm is demonstrated via numerical results.

Smart buildings provide an important opportunity for large-scale development of demand response, due to their existing flexibility that can be harvested through Internet-of-Things technologies with minimal cost of new equipment. However, after taking an energy management action, the resulting energy consumption of a building depends on several uncertain factors. Thus, the consumption of the smart building is not directly controllable and, contrary to the typical approach taken in the literature, it cannot be modeled as a decision variable in practice. In this paper, we consider the problem of coordinating the stochastic load control actions of multiple smart buildings under such endogenous uncertainties. We model the problem as a Multi-agent Markov Decision Process and, after reformulations, we bring it to a solvable decomposed form. Our simulations compare the proposed approach with a myopic approach that does not consider future uncertainties, and also quantify the trade-off between cost-effectiveness and computational time in terms of the look-ahead horizon length.

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.