Adaptive Coordination of Distributed Energy Resources in Lossy Power Distribution Systems

This paper is concerned with the problem of coordinating a set of distributed energy resources (DERs) in a lossy power distribution system to provide frequency regulation services to a bulk power grid with the explicit consideration of system losses. To this end, we formulate the problem as an optimization problem, the objective of which is to minimize some cost function subject to a set of constraints. The formulation requires knowledge of incremental total system losses, which we approximate using the so-called loss factors (LFs) that explicitly capture the impacts of both active and reactive power injections on system losses. The LFs are estimated recursively using power injection measurements; thus, they are adaptive to various phenomena that impact the power system operation. Numerical simulation on a 33-bus distribution test feeder validated the effectiveness of the proposed framework

Learning Dynamical Demand Response Model in Real-Time Pricing Program

Price responsiveness is a major feature of end use customers (EUCs) that participate in demand response (DR) programs, and has been conventionally modeled with static demand functions, which take the electricity price as the input and the aggregate energy consumption as the output. This, however, neglects the inherent temporal correlation of the EUC behaviors, and may result in large errors when predicting the actual responses of EUCs in real-time pricing (RTP) programs. In this paper, we propose a dynamical DR model so as to capture the temporal behavior of the EUCs. The states in the proposed dynamical DR model can be explicitly chosen, in which case the model can be represented by a linear function or a multi-layer feedforward neural network, or implicitly chosen, in which case the model can be represented by a recurrent neural network or a long short-term memory unit network. In both cases, the dynamical DR model can be learned from historical price and energy consumption data. Numerical simulation illustrated how the states are chosen and also showed the proposed dynamical DR model significantly outperforms the static ones.Comment: Accepted to IEEE ISGT NA 201

Data-driven coordination of assets in power distribution systems for ancillary service provision

In this dissertation, we develop several data-driven frameworks for coordinating distributed energy resources (DERs) in power distribution systems to provide ancillary services including active power provision and reactive power regulation. The proposed frameworks generally consist of three components, namely (i) an input-output (IO) model of the system describing the relation between the variables of interest to the problem, (ii) an estimator that provides estimates of the parameters that populate the model in (i), and (iii) a controller that uses the model in (i) with the parameters estimated via (ii) to determine the active and/or reactive power injection set-points of the DERs by solving the optimal DER coordination problem (ODCP), which is cast as a static optimization problem. We develop efficient estimation algorithms that utilize measurements to estimate the parameters in the IO model. Special emphasis is devoted to algorithms that address the potential collinearity issue in the measurements, and formulations that significantly reduce the number of parameters to be estimated. The idea of data-driven coordination is also applied to address the problem of coordinating load tap changers (LTCs)---an important class of assets used for voltage control in distribution networks---using only measurements of voltage magnitudes. Different from the ODCP that is cast as a static optimization problem, the optimal LTC coordination problem is cast as a multi-stage decision-making problem and formulated as a Markov decision process (MDP), in which the unknown power injections are modeled as uncertainty sources. The MDP is solved via a reinforcement learning algorithm to obtain a control policy that maps the voltage magnitude measurements to the optimal tap positions. The data-driven nature makes the proposed frameworks intrinsically adaptive and robust to changes in operating conditions and power distribution system models, which are illustrated via extensive case studies