Energy and power management system for microgrids of large‐scale building prosumers

Abstract

A method for optimal energy and power management of microgrids consisting of mega buildings, plug-in electric vehicles (PEVs) and renewable energy sources (RES) with low computation requirements is proposed by the authors. Thermal and electrical loads are considered for the operation scheduling of the microgrid. In case of non-interconnected operation of the microgrid with the main power grid, the proposed method allows the microgrid to meet the power demand by the buildings and distribution loads exploiting only the hosted PEVs, the integrated RES and, if it is necessary or financially optimal, building auxiliary diesel generators. The primary goal of the suggested algorithm is to significantly reduce the overall daily cost of the microgrid's operation while simultaneously meeting a wide range of constraints. The implementation of the method is based on the exploitation of a two-level hierarchical multi-agent system (MAS) at the level of the microgrid. Suitably defined flexibilities of the microgrid's components to change their power are used to implement optimal power dispatch to them. Detailed simulation results indicated that a remarkable cost reduction of 27% can be achieved.

Optimal Cluster Scheduling of Active–Reactive Power for Distribution Network Considering Aggregated Flexibility of Heterogeneous Building-Integrated DERs

“…The active-reactive power collaborative optimization scheduling within clusters can be formulated as a multi-object optimization model based on the flexibility of buildingintegrated flexible DERs, while the uncertainties in PV outputs, ES performance, EV parking behaviors, TCL heating/cooling behaviors, and SVC regulation capacity are considered and represented by various stochastic scenarios based on historical data in [3,30,31]. Specifically, each cluster k aims to minimize operational cost f 1 k , power-loss cost f 2 k , and penalty cost for flexibility deficiency f 3 k , as shown in (11a), (12), and (13a). f 1 k is composed of the maintenance cost C PV k,i,t,s of BIPVs due to the renewable generation curtailment, the lifetime degradation cost C ES k,i,t,s of BIESs, the compensation cost C CL k,i,t,s of BIEVs and BITCs stemmed from power regulation and the operation cost C SVC k,i,t,s of static var compensator (SVC) under scenario s. f 2 k can be derived from the power-flow calculation during the scheduling circulation, where U k,i,t,s , G k,i,t,s , B k,i,t,s , and δ k,ij,s represent the nodal voltage magnitudes, the conductance, the susceptance, and the phase angle of line ij at time t within cluster k under scenario s; λ Loss k,s is the compensation price of the network loss.…”

“…A method for the coordinated optimal operation scheduling of active distribution networks was developed to meet the electricity demand of the building itself with interconnected electric vehicles and integrated RESs [11]. To implement optimal power dispatch and significantly reduce the overall operation cost of the microgrid, a method for optimal energy and power management of microgrids consisting of mega buildings, plug-in electric vehicles (PEVs), and renewable energy sources (RESs) was designed [12]. Furthermore, an energy management system (EMS) for microgrids of building prosumers based on a hierarchical multi-agent system (MAS) was studied in [13] to minimize the operation cost of a microgrid, considering many operation and technical constraints.…”