A Multi Time-Scale and Multi Energy-Type Coordinated Microgrid Scheduling Solution—Part II: Optimization Algorithm and Case Studies

Abstract

In part II of this two-part paper, the improved particle swarm optimization (IPSO) algorithm for solving the microgrid (MG) day-ahead cooling and electricity coordinated scheduling is proposed. Significant improvements are made in comparison with the conventional PSO algorithm from two aspects. First, the mandatory correction is implemented to ensure the complex coupled constraints among the components of a particle are met after the particle's position is updated, which could enhance the algorithm performance when solving the problem including complex constraints. Second, it is assumed that a solution denoted by a particle occupies a neighboring area, the size of which decreases from a certain value to nearly zero as the iteration step increases to its limitation, which helps to avoid the pre-maturity of algorithm. For an MG composed of the combined cooling, heating and power (CCHP) units, PV panels, wind turbines, and storage batteries, a range of case studies under different MG operating modes are carried out through simulations. The simulation results demonstrate the proposed multi time-scale, multi energy-type coordinated MG scheduling solution can achieve the co-optimization of multi energy-type supply to meet customer's cooling and electricity demands, and make the MG be controllable as seen from the connected main grid.

Risk-minimizing stochastic self-scheduling model for microgrid in day-ahead electricity market

“…To model the stochastic behavior of the microgrid's forecast uncertainties, 100 scenarios are generated with ARMA time series model, based on the procedure delineated in the Figure 1, for each of wind speed, microgrid load, and LMP of PCC. The optimal ARMA model for wind speed is ARMA (12,11), and for microgrid load and PCC's LMP is ARMA (11,11), which are obtained through minimization of error variance based on the mentioned historical data. The 100 scenarios generated for wind speed, PCC's LMP, and microgrid load are shown in Figure 4 The scenarios of each quantity are normalized by dividing to its maximum value.…”

“…They have also proposed models for combined cooling, heating, and power for taking into account in their day-ahead scheduling problem, which is solved by an improved particle swarm optimization method presented in Ref. [11]. To coordinate the distributed generations with energy storage devices, a smart energy management system is designed in Ref.…”

Optimal Real-Time Operation Strategy for Microgrid: An ADP-Based Stochastic Nonlinear Optimization Approach

“…To formulate the optimization problem for the real-time operation of the microgrid, both the network and the devices need to be modeled. To simplify the problem, network power flow constraints are usually neglected in most of the prior works [5]- [7], [13], [14]. DC power flow is widely adopted in transmission network [15], [16], but it is not suitable for the distribution system level [17].…”

“…Although MINLP solvers such as CONOPT and BARON are commercialized, they cannot find solutions in reasonable times even for a small-scale system [25]. Some meta-heuristic methods [7]- [10] such as particle swarm optimization (PSO) have been employed, yet the computational burden rises exponentially with the number of variables and constraints. The hierarchical optimization methods [26] are also used, but the global optimality cannot be guaranteed.…”

Adaptively Constrained Stochastic Model Predictive Control for the Optimal Dispatch of Microgrid

“…However, a proliferation of RESs, such as photovoltaic panels (PVs) and wind turbines (WTs), brings significant challenges due to their inherent intermittent nature. Fortunately, microgrid (MG) is emerging as a new concept of operation in distribution systems, which is required to present as a single, self-controlled entity to the surrounding distribution grid [3,4]. In China, based on the forecasts of electricity demands and RESs' power outputs, the power exchange between MG and the connected distribution grid is optimized in advance considering certain economic objectives and is expected to remain unchanged in real-time operation in spite of random variations of RESs and electric loads, by properly controlling energy storage units in the MG [3,4].…”