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Publication# A VSC-Based BESS Model for Multi-Objective OPF Using Mixed Integer SOCP

Résumé

This paper presents a new model of voltage source converter (VSC)-based battery energy storage systems (BESSs) that interface with power grids. A VSC-based BESS is made up of a series connection of a VSC, its connecting transformer and a BESS. The VSC allows a BESS to generate both active and reactive powers in all four quadrants. The proposed model captures the coupling between active power, reactive power and the voltage of a BESS. In addition, the proposed model explicitly describes the relationship between the control configuration of Pulse Width Modulation (PWM)-VSC and the power output of the BESS. Therefore, the proposed model possesses unparalleled control capabilities in the operational parameters of both the AC and DC sides of the converter. By incorporating such a model into the active- reactive optimal power flow ( A-R-OPF) formulation, we can not only optimize the active and reactive power output of the BESS in power systems, but also understand how the optimal powers are generated by setting the operational parameters of both the BESS and PWM-VSC. To solve the A-R-OPF problem with the proposed model, we propose a sequence of strong relaxations to transform the problem into a mixed-integer second order cone programming (SOCP) problem. Such a formulation is amendable for efficient solutions using off-the-shelf solvers. Case studies on the IEEE benchmark systems show that more than 17.73% of power losses in transmission lines and more than 0.961% of interface losses can be reduced by using the proposed model in comparison to the traditional BESS model.

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This paper presents a new model of voltage source converter (VSC) based battery energy storage systems (BESSs) that interface with power grids. A VSC-based BESS is made up of a series connection of a VSC, its connecting transformer, and a BF-SS. The VSC allows a BF-SS to generate both active and reactive powers in all four quadrants. The proposed model captures the coupling between active power, reactive power, and the voltage of a BESS. In addition, the proposed model explicitly describes the relationship between the control configuration of pulsewidth modulation (PWM) VSC and the power output of the BESS. Therefore, the proposed model possesses unparalleled control capabilities in the operational parameters of both the ac and dc sides of the converter. By incorporating such a model into the active-reactive optimal power flow (A-R-OPF) formulation, we can not only optimize the active and reactive power output of the BESS in power systems, but also understand how the optimal powers are generated by setting the operational parameters of both the BESS and PWM-VSC. To solve the A-R-OPF problem with the proposed model, we propose a sequence of strong relaxations to transform the problem into a mixed-integer second-order cone programming problem. Such a formulation is amendable for efficient solutions using off-the-shelf solvers. Case studies on the IEEE benchmark systems show that more than 17.73% of power losses in transmission lines and more than 0.961% of interface losses can be reduced by using the proposed model in comparison to the traditional BESS model.

Mario Paolone, Paola Pongiglione, Fabrizio Sossan

This paper presents a method to determine the optimal location, energy capacity, and power rating of distributed battery energy storage systems at multiple voltage levels to accomplish grid control and reserve provision. We model operational scenarios at a one-hour resolution, where deviations of stochastic loads and renewable generation (modeled through scenarios) from a day-ahead unit commitment and violations of grid constraints are compensated by either dispatchable power plants (conventional reserves) or injections from battery energy storage systems. By plugging-in costs of conventional reserves and capital costs of converter power ratings and energy storage capacity, the model is able to derive requirements for storage deployment that achieve the technical-economical optimum of the problem. The method leverages an efficient linearized formulation of the grid constraints of both the HV (High Voltage) and MV (Medium Voltage) grids while still retaining fundamental modeling aspects of the power system (such as transmission losses, effect of reactive power, OLTC at the MV/HV interface, unideal efficiency of battery energy storage systems) and models of conventional generator. A proof-of-concept by simulations is provided with the IEEE 9-bus system coupled with the CIGRE’ benchmark system for MV grids, realistic costs of power reserves, active power rating and energy capacity of batteries, and load and renewable generation profile from real measurements.

2020Rahul Kumar Gupta, Mario Paolone, Enrica Scolari, Fabrizio Sossan

The capacity of electrical distribution systems of hosting photo-voltaic (PV) generation is limited due to the requirements of distribution system operators (DSOs) to respect statutory voltage levels along feeders and not exceed line current limits. Traditional ways to perform voltage regulation in distribution systems are on- load tap changers, voltage series regulators, and coordinated control of the reactive power set-points of PV converters, that however is ineffective in low voltage distribution systems due to the large R/X ratio of the longitudinal parameters of lines. The problem of line current congestions is normally tackled by curtailing PV generation, a practice that is however inefficient because it decreases the capacity factor of PV plants. Thanks to their decreasing cost, battery energy storage systems are gaining of interest as they can provide both voltage control and congestion management, avoiding the use of multiple countermeasures among those listed above. Distributed battery energy storage systems for grid control could be owned and operated by distribution system operators directly, and become a new safeguarding asset for grids. The battery fleet and control infrastructure can be designed to meet industrial-grade operational standards for control accuracy, reliability, and maintenance. This feature would not be possible with behind-the-meter PV self-consumption equipment installed by end customers (which can also relieve grid congestions by promoting the consumption of locally generated electricity) because this asset would not belong to the operator. In this report, we describe a method to plan the deployment of grid-connected batteries in distribution systems with the objective of accommodating a target level of PV generation capacity. The method deter- mines the location, energy capacity and power rating of the batteries with the minimum capital costs such that their injections can restore suitable nodal voltages and line currents in the network. The method is tractable thanks to leveraging an exact convex formulation of the optimal power flow problem and a convex battery model that includes the notion of charging/discharging efficiency. We apply the method to a low voltage (LV) and medium voltage (MV) distribution network, modeled according to the specifications of the CIGRE’s benchmark systems. The analysis is carried out for different levels of installed distributed PV generation capacity, from zero up to 3 times the generation hosting capacity of the grid. Uniform clear-sky conditions are considered as they are conducive to the largest yield from distributed PV generation. In the considered case studies, it is shown that for very large values of PV penetration levels (i.e., twice the PV hosting capacity), the total power rating of the deployed battery systems grows in a 1-to-1 ratio with the installed PV capacity. For less extreme values of installed PV capacity, the growth is generally smaller. The total energy capacity of batteries grows faster than for the power rating due to the typical peak production patterns of PV generation, that typically occur in the middle of the day and last for several hours. In particular, once the injection from a PV plant determines an over-current or over-voltage, it needs to be postponed and stored until it persists (e.g., hours), thus determining large energy capacity requirements. Using distributed battery storage to Research supported by the ”joint activities on scenarios and modelling” program of the Swiss competence center on energy research (SCCER-JASM) 1 mitigate grid congestions caused by distributed PV generation is an energy-intensive application that can be coupled with power-intensive applications, like primary frequency regulation, by leveraging algorithms for the provision of multiple ancillary services.

2018