Showing papers by "Alessandra Parisio published in 2021"

Optimal Virtual Power Plant Management for Multiple Grid Support Services

Abstract: A hierarchical control architecture is proposed for the optimal day-ahead commitment of multiple grid support services within a virtual power plant (VPP). The day-ahead optimization considers pricing and cost data to determine the commitment schedule, and a robust Model Predictive Control (MPC) approach is included to minimize the unbalance fees during real-time operations. The multi-level control has been demonstrated experimentally using a hybrid test system, where the VPP is formed of a commercial 240 kW, 180 kWh battery energy storage system (BESS), while the additional assets are modelled in a real-time digital simulator (RTDS). Two case studies are analyzed: the first assumes a purely-electrical VPP, with a single connection to the public network; the second involves a multi-energy approach, with the introduction of a gas-supplied Combined Heat and Power unit (CHP). Both winter and summer price scenarios are tested. The results show the superiority of the multiple-service operation compared to providing a single grid support service. For example, the net revenue is increased by 30% (winter) and 7% (summer) when compared to just frequency regulation, and by +99% (winter) and 30% (summer) when compared to only energy arbitrage.

A Model Predictive Control Approach to Operation Optimization of an Ultracapacitor Bank for Frequency Control

Abstract: This paper presents a nonlinear dynamic simulation model of an ultracapacitor (UC) bank and the associated control system. The control system at hand consists of two levels: the lower level controls the inverter of the UC bank, while the upper control level is responsible for providing charging/discharging active power set points to be followed by the lower control level. This paper focuses on the development of the upper control level for frequency control. Specifically, two simulation case studies are developed so as to assess the performance of the proposed control framework. In the first case study the upper control level is developed using a classical Proportional-Integral-Derivative (PID) controller. In the second case study the upper control level is devised using a Model Predictive Control (MPC) algorithm based on internal linear prediction model of a nonlinear UC bank. In both cases, a nonlinear UC bank simulation model is used. The simulation case studies are modelled and tested in Matlab/Simulink. The response of the MPC-controlled UC bank is compared to the 3 existing PID-control algorithms for frequency control. The simulation results show that the MPC algorithm outperforms the conventional PID controllers.

Charge/Discharge Event Model for Electric Vehicles with Predictive Control Application

Abstract: Electric vehicles (EV)s are acknowledged as key technologies to decarbonize the transportation sector and counteract variable power generation. However, in order for EVs to have an impact on the power system, a large number of them must be managed and aggregated, and this requires an efficient modeling of their charging/discharging operations. In this paper, EVs charge/discharge operations are modeled as charging/discharging events. The modeling approach is based on historical utilization of charging stations and allows individual EV owners to opt in/out of bidirectional energy exchange once they connect their vehicles to the energy network. Binary decision variables are used to monitor vehicles’ presence in the network and owner’s charging preference. The energy network is modeled using a control oriented modeling framework derived from the energy hub concept. This novel EV modeling approach is integrated into the modeling framework and used in the formulation of an optimization problem, leading directly to the design of a model predictive controller. Using this scheme, EV presence and state-of-charge can be continuously monitored and assessed, and predictions of future EV charging/discharging events can be efficiently incorporated.

Decentralised Predictive Control of Multi-Energy Resources in Buildings

Abstract: This work concerns model predictive control of multi-energy buildings with shared network capacity constraints. More specifically, it is concerned with how to effectively coordinate a large number of locally constrained and coupled buildings without violating indoor comfort and network constraints, whilst maintaining the privacy of building owners. Buildings are regarded as agents and modelled using a hybrid system approach within a multi-energy context. The developed models consider buildings connected to constrained electricity and gas supply networks. Building energy resources can include battery devices, heat-pumps or micro-turbine based combined heat and power units to satisfy both the local electricity and heating demands. The building and network modelling is incorporated into a predictive control scheme to optimally coordinate multiple buildings, with the objective of minimising individual building gas and electricity costs. However, for large systems consisting of many buildings, the computation time required to coordinate and optimise building operations in a centralised manner becomes prohibitive. Hence, in order to devise a scalable control framework, a decentralised approach is employed, where each building agent is required to solve a tractable Mixed Integer Linear Program at each time step. By doing so, multiple buildings are cost-effectively coordinated and operated so as to achieve a common target with significantly reduced computational time. The proposed approach can handle an arbitrarily large number of buildings and is validated through a relevant case study.