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Publication# Characterizing the Reserve Provision Capability Area of Active Distribution Networks: A Linear Robust Optimization Method

Résumé

Distributed energy resources (DERs) installed in active distribution networks (ADNs) can be exploited to provide both active and reactive power reserves to the upper-layer grid (i.e., sub-transmission and transmission systems) at their connection point. This paper introduces a method to determine the capability area of an ADN for the provision of both active and reactive power reserves while considering the forecast errors of loads and stochastic generation, as well as the operational constraints of the grid and DERs. The method leverages a linearized load flow model and introduces a set of linear scenario-based robust optimization problems to estimate the reserve provision capability (RPC) area of the ADN. It is proved that, under certain assumptions, the RPC area is convex. The performance of the proposed method is tested on a modified version of the IEEE 33-bus distribution test system.

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Environmental concerns are leading to a fast transition in electric power systems towards replacing fossil fuel and nuclear generation with renewable generation. This transition has two significant implications for electric power systems:1-The capacity of conventional dispatchable electric power plants, which are the main providers of power flexibility , is sharply falling. It is notable that the security of electric power systems can be preserved if and only if there is an adequate amount of power flexibility to guarantee the frequency stability, voltage regulation, power quality, and congestion management.2-The share of renewable energy sources (RESs) in the electric power generation portfolio is steeply soaring. It raises a significant stress on electric power systems due to the fact that the generated electricity from RESs is intrinsically intermittent and accompanied with uncertainties.The simultaneous realization of these two issues puts electric power systems in a turning point. This thesis devotes a deep attention to the power flexibility provision issue with the purpose of empowering transmission system operators (TSOs) and distribution system operators (DSOs) to steer electric power systems securely in this emerging architecture. To this end, it sets out to unlock the potential power flexibility of distributed energy resources (DERs) located in distribution networks. The first part of the thesis deals with the problem from TSO's perspective. It develops two decision making tools for TSOs to help them optimally quantify their required power flexibility from flexibility providers (including flexible distribution networks) in a decentralized energy and flexibility market structure. The first tool concentrates solely on active power flexibility and accordingly offers a framework to model aggregated active power flexibility of distribution networks from TSO's point of view. In contrast, the second tool introduces a holistic TSO-DSO coordination framework to enable TSO and DSO to exchange both active/reactive power flexibility. Both developed tools employ mathematical techniques to cast the TSO's decision making process as a two-stage linear stochastic optimization problem while accounting for risk associated to the uncertainties. The frameworks lay a ground for TSO and DSOs to exchange power flexibility without having to reveal their confidential grids data.The second part of the thesis deals with the problem from DSO's perspective. It first concentrates on an active distribution network (ADN) and constructs a set of linear scenario-based robust optimization problems to characterize the maximum active/reactive powers flexibility that the ADN can provide upon request at its point of common coupling (PCC) to the upper-layer grid. Second, it constructs a linear grid&uncertainties-cognizant ADN management method to determine the amount of active/reactive powers flexibility that should be provided by each DER during the real-time operation in such a way that the ADN can provide, with minimum deviation, the active/reactive powers flexibility requested by the upper-layer grid at the PCC.In addition to dealing with the problem from both TSO's and DSO's perspectives, a privileged feature of the thesis is that it offers linear tractable algorithms to deal with all above-mentioned tasks while considering grid's constraints and uncertainties stemming from the forecast errors of demand and renewable generation.

Rahul Kumar Gupta, Mario Paolone, Antonio Zecchino

The core of this activity is to provide distribution system operators with tools for the optimal and grid-aware operation of utility-scale distributed battery energy storage systems (BESSs) in order to optimize the integration of stochastic distributed generation. The ultimate goal is the optimal control of active distribution networks with high penetration of stochastic (i.e., non-controllable) renewable-based generation. In particular, unscheduled fluctuations of the power exchanged with the upper grid level are minimised via the proposed control and scheduling framework, where we compute a dispatch plan in day-ahead using advanced forecasts of the aggregated prosumption and track it during the real-time operation using a grid-aware optimal power flow (OPF)-based control of the controllable BESS accounting for both grid constraints and BESS operational constraints. We experimentally validated the proposed control and scheduling strategy to dispatch the operation of a medium voltage active distribution network interfacing stochastic heterogeneous prosumers by using a grid-connected BESS as a controllable element coupled with a distributed monitoring infrastructure. In particular, the framework consists of two algorithmic layers. In the first one (day-ahead scheduling), an aggregated dispatch plan is determined, which is based on the day-ahead forecast of the prosumption and accounts for the operational constraints of grid and BESS state-of-energy. An adaptive data-driven scheme based on multi-variate Gaussian distribution is used to forecast the power consumption and photovoltaic generation and used as an input at the day-ahead stage. Then, the dispatch plan for the next 24 hours is computed using a scenariobased iterative AC OPF (Codistflow) algorithm, which accounts for forecasts of RESs and load profiles with 95% confidence interval with 1h time resolution. The second layer consists of real-time operation, where a grid-aware model predictive control determines the active and reactive power set-points of the BESS so that their aggregated contribution tracks the dispatch plan while obeying to BESS’s operational constraints as well as the grid’s ones. The grid constraints are modelled using the Augmented Relaxed OPF developed at the EPFL-DESL. The proposed control framework is validated by dispatching the operation of a 12kV/20MVA MV distribution network in Aigle, Switzerland (i.e. the REeL demonstrator) using a 1.5 MW/2.5 MWh BESS, which is controlled in real-time given the online grid state estimation enabled by the deployed distributed PMU-based sensing infrastructure.

Mokhtar Bozorg, Mohsen Kalantar Neyestanaki, Fabrizio Sossan

The flexibility of distributed energy resources (DERs) accommodated in active distribution networks (ADNs) can be aggregated and then used to provide ancillary services to the transmission system. In this context, this paper presents a linear optimization method for the transmission system operator (TSO) to allocate its required active power reserve from aggregated resources installed in active distribution systems (ARADSs) as well as dispatchable bulk power plants (DBPPs). It consists in a linear optimization problem that minimizes the sum of the expected cost of active power reserve allocated from all possible providers (including ARADSs and DBPPs) and the expected cost of load not served over a desired time horizon. The value of lost load (VOLL) index is used as a criterion to realize an economical balance between the expected cost of allocated reserve and expected cost of load not served. The method leverages scenarios of power system contingencies and forecast errors of loads and renewable generation to represent typical operational uncertainties. A simulation proof-of-concept using real-data from the transmission system operator of Switzerland, Swissgrid, is provided to illustrate the performance of the method.