skip to main content

Title: Strategic Behavior of Distributed Energy Resources in Energy and Reserves Co-Optimizing Markets
We consider decentralized scheduling of Distributed Energy Resources (DERs) in a day-ahead market that clears energy and reserves offered by both centralized generators and DERs. Recognizing the difficulty of scheduling transmission network connected generators together with distribution feeder connected DERs that have complex intertemporal preferences and dynamics, we propose a tractable distributed algorithm where DERs self-schedule based on granular Distribution Locational Marginal Prices (DLMPs) derived from LMPs augmented by distribution network costs. For the resulting iterative DER self-scheduling process, we examine the opportunity of DERs to engage in strategic behavior depending on whether DERs do or do not have access to detailed distribution feeder information. Although the proposed distributed algorithm is tractable on detailed real-life network models, we utilize a simplified T&D network model to derive instructive analytical and numerical results on the impact of strategic DER behavior on social welfare loss, and the distribution of costs and benefits to various market participants.
Authors:
; ;
Award ID(s):
1733827
Publication Date:
NSF-PAR ID:
10208066
Journal Name:
2018 IEEE Conference on Decision and Control (CDC), Miami Beach, FL, 2018, pp. 4875-4881. doi: 10.1109/CDC.2018.8619550
Page Range or eLocation-ID:
4875 to 4881
Sponsoring Org:
National Science Foundation
More Like this
  1. We consider radial distribution networks hosting Distributed Energy Resources (DERs), including Solar Photo­voltaic (PV) and storage-like loads, such as Electric Vehicles (EVs). We employ short-run dynamic Distribution Locational Marginal Costs (DLMCs) of real and reactive power to co­optimize distribution network and DER schedules. Striking a balance between centralized control and distributed self­dispatch, we present a novel hierarchical decomposition ap­proach that is based on centralized AC Optimal Power Flow (OPF) interacting with DER self-dispatch that adapts to real and reactive power DLMCs. The proposed approach is designed to be highly scalable for massive DER Grid integration with high model fidelity incorporating rigorous network component dynamics and costs and reffecting them in DLMCs. We illustrate the use of an Enhanced AC OPF to discover spatiotemporally varying DLMCs enabling optimal Grid-DER coordination in­corporating congestion and asset (transformer) degradation. We employ an actual distribution feeder to exemplify the use of DLMCs as financial incentives conveying sufficient information to optimize Distribution Network and DER (PV and EV) operation, and we discuss the applicability and tractability of the proposed approach, while modeling the full complexity of spatiotemporal DER capabilities and preferences.
  2. Dispatching a large fleet of distributed energy resources (DERs) in response to wholesale energy market or regional grid signals requires solving a challenging disaggregation problem when the DERs are located within a distribution network. This manuscript presents a computationally tractable convex inner approximation for the optimal power flow (OPF) problem that characterizes a feeders aggregate DERs hosting capacity and enables a realtime, grid-aware dispatch of DERs for radial distribution networks. The inner approximation is derived by considering convex envelopes on the nonlinear terms in the AC power flow equations. The resulting convex formulation is then used to derive provable nodal injection limits, such that any combination of DER dispatches within their respective nodal limits is guaranteed to be AC admissible. These nodal injection limits are then used to construct a realtime, open-loop control policy for dispatching DERs at each location in the network to collectively deliver grid services. The IEEE-37 distribution network is used to validate the technical results and highlight various use-cases.
  3. The rapid growth of distributed energy resources (DERs) is one of the most significant changes to electricity systems around the world. Examples of DERs include solar panels, small natural gas-fueled generators, combined heat and power plants, etc. Due to the small supply capacities of these DERs, it is impractical for them to participate directly in the wholesale electricity market. We study in this paper an efficient aggregation model where a profit-maximizing aggregator procures electricity from DERs, and sells them in the wholesale market. The interaction between the aggregator and the DER owners is modeled as a Stackelberg game: the aggregator adopts two-part pricing by announcing a participation fee and a per-unit price of procurement for each DER owner, and the DER owner responds by choosing her payoff-maximizing energy supplies. We show that our proposed model preserves full market efficiency, i.e., the social welfare achieved by the aggregation model is the same as that when DERs participate directly in the wholesale market.
  4. Renewable portfolio standards are targeting high levels of variable solar photovoltaics (PV) in electric distribution systems, which makes reliability more challenging to maintain for distribution system operators (DSOs). Distributed energy resources (DERs), including smart, connected appliances and PV inverters, represent responsive grid resources that can provide flexibility to support the DSO in actively managing their networks to facilitate reliability under extreme levels of solar PV. This flexibility can also be used to optimize system operations with respect to economic signals from wholesale energy and ancillary service markets. Here, we present a novel hierarchical scheme that actively controls behind-the-meter DERs to reliably manage each unbalanced distribution feeder and exploits the available flexibility to ensure reliable operation and economically optimizes the entire distribution network. Each layer of the scheme employs advanced optimization methods at different timescales to ensure that the system operates within both grid and device limits. The hierarchy is validated in a large-scale realistic simulation based on data from the industry. Simulation results show that coordination of flexibility improves both system reliability and economics, and enables greater penetration of solar PV. Discussion is also provided on the practical viability of the required communications and controls to implement the presented schememore »within a large DSO.« less
  5. Transactive Energy (TE) is an emerging discipline that utilizes economic and control techniques for operating and managing the power grid effectively. Distributed Energy Resources (DERs) represent a fundamental shift away from traditionally centrally managed energy generation and storage to one that is rather distributed. However, integrating and managing DERs into the power grid is highly challenging owing to the TE implementation issues such as privacy, equity, efficiency, reliability, and security. The TE market structures allow utilities to transact (i.e., buy and sell) power services (production, distribution, and storage) from/to DER providers integrated as part of the grid. Flexible power pricing in TE enables power services transactions to dynamically adjust power generation and storage in a way that continuously balances power supply and demand as well as minimize cost of grid operations. Therefore, it has become important to analyze various market models utilized in different TE applications for their impact on above implementation issues.In this demo, we show-case the Transactive Energy Simulation and Analysis Toolsuite (TE-SAT) with its three publicly available design studios for experimenting with TE markets. All three design studios are built using metamodeling tool called the Web-based Graphical Modeling Environment (WebGME). Using a Git-like storage and tracking backendmore »server, WebGME enables multi-user editing on models and experiments using simply a web-browser. This directly facilitates collaboration among different TE stakeholders for developing and analyzing grid operations and market models. Additionally, these design studios provide an integrated and scalable cloud backend for running corresponding simulation experiments.« less