Stochastic Model Predictive Control Based Scheduling Optimization of Multi-Energy System Considering Hybrid CHPs and EVs

Abstract

Recently, the increasing integration of electric vehicles (EVs) has drawn great interest due to its flexible utilization; moreover, environmental concerns have caused an increase in the application of combined heat and power (CHP) units in multi-energy systems (MES). This paper develops an approach to coordinated scheduling of MES considering CHPs, uncertain EVs and battery degradation based on model predictive control (MPC), aimed at achieving the most economic energy scheduling. After exploiting the pattern of the drivers’ commuting behavior, the stochastic characteristics of available charging/discharging electric power of aggregated EVs in office or residential buildings are analyzed and represented by the scenarios with the help of scenario generation and reduction techniques. At each step of MPC optimization, the solution of a finite-horizon optimal control is achieved in which a suitable number of available EVs scenarios is considered, while the economic objective and operational constraints are included. The simulation results obtained are encouraging and indicate both the feasibility and the effectiveness of the proposed approach.

A Comprehensive Review of the Design and Operation Optimization of Energy Hubs and Their Interaction with the Markets and External Networks

“…PV systems [23,27,38,40,41,[43][44][45]48,[51][52][53][54][55][56][57][58][59][60][61] CHP [12,14,17,19,[21][22][23][24][25][26][27][28][29][30][31] H 2 generator from fossil fuels [50] P2G [23] Heat recovery from CHP [33][34][35][36][37][39][40][41][42][43][44]52,59] Wind turbines [48,51,…”

“…In order to ensure that at 8 a.m., the EV has a good charge level, a dissatisfaction term is considered that is directly proportional to the difference between the maximum level of charge and the level of charge at 8 a.m. Other works divide the PEVs into clusters [54], each with specific characteristics: (1) battery capacity, (2) arrival and departure times at/from the charging stations, (3) the state-of-charge (SOC) at the arrival time, and (4) the SOC desired at the departure time. Some works, such as [33], use a probabilistic approach representing the EVs' availability by a normal distribution and a probability function based on survey data. In addition, these data are used in a stochastic model predictive control (MPC) to control the local energy system, allowing for planning for the EV availability with refined scenarios for each hour.…”

“…Several papers address the problem of EH optimization using MILP formulations, such as [19][20][21][22][23][26][27][28][30][31][32][33][34][35]47,50,63,67,68,72,74,81,82,90,95,104,105,[111][112][113][114][115][117][118][119][120][121]. In particular, Ref.…”

Intelligent Energy Management of Electrical Power Systems

“…Taking into account the longest life cycle and the optimum economic efficiency, the proposed model provides effective decision-making support for designing the optimal plan for system operation. Aimed at achieving the most economic energy scheduling and based on stochastic model predictive control, [2] develops an interesting approach to coordinate scheduling of multi-source/multi-energy system. Scenarios for aggregated electric vehicles (EVs) and their battery degradation are taken into account and a finite-horizon optimal control solution is presented while the economic objective and operational constraints are included.…”

Optimal Management of a Smart Port with Shore-Connection and Hydrogen Supplying by Stochastic Model Predictive Control

“…The management of this hypothetical future smart port, is realized by a stochastic Model Predictive Control (MPC) algorithm. Literature provides several uses of MPC in smart cities with Power to Gas (PtG) devices [10] or, in general, for MES [11]. The peculiarity of the proposed approach is that uncertainties of RES are considered to maximize the economical earning, dispatching the compensation of forecast errors to the available storage systems, i.e.…”