Stochastic model predictive control operation strategy of integrated energy system based on temperature‐flowrate scheduling model considering detailed thermal characteristics

Abstract

Summary A scheduling model is a prerequisite for an operation strategy of integrated energy system (IES). Existing scheduling models of IES, however, are typically based on heat‐transfer variables either completely or partially, which oversimplify detailed thermal characteristics. To this end, a novel scheduling model is proposed where all thermal processes are modeled by temperature and flowrate of working fluids. This improvement renders the capability to the scheduling model to incorporate different thermal processes. Furthermore, the nonlinear product terms of temperature and flowrate in the proposed model are linearized by the binary expansion method. Based on the linearized scheduling model, a stochastic model predictive control (SMPC) operation strategy is exploited to optimize the economic performance by energy forecast, scenario reduction, rolling optimization, and feedback correction. Afterwards, four operation modes considering different temperature changes of the devices, networks, and the environment are performed and compared. The results found that thermal characteristics will affect device operation results and the degree of influence varies. The network temperature changes have the broadest influence, followed by the device and the ambient temperature changes. Moreover, system operation costs are also affected by detailed thermal characteristics. The total cost, the gas cost, and the electricity cost under Mode 2 are almost the same to those of Mode 1. However, the first two costs are reduced by 3.4% and 5.3% under Mode 3, and are reduced by 2.7% and 4% under Mode 4, despite that the electricity cost increases by 0.2% under Mode 3 and remains almost the same under Mode 4. These indicate that reliability and economy of an IES are affected by thermal characteristics, and it is thus the necessity to consider detailed thermal characteristics in an operation. Moreover, the results demonstrate the capability of the generalized temperature‐flowrate based scheduling model and the effectiveness of the SMPC operation strategy.

A Variable Performance Parameters Temperature–Flowrate Scheduling Model for Integrated Energy Systems

“…In order to prove the superiorities of the proposed model, a comparative study is conducted by applying two models. Case 1 is the scheduling model in [32] and Case 2 is our proposed method, the essential difference is: Case 1: temperature and flowrate are taken as decision variables, but all the performance parameters are all treated as constants. The COP i EC , COP i HP , η GT , η W HB are assumed to be 6.0, 5.0, 28%, and 80%, respectively.…”

“…Lv [30] established an optimal operation of heat network and buildings in the form of the temperature and flowrate, and Gu [31] proposed a scheduling model considering the transmission process together with the thermal inertia of buildings with temperature parameters. Wei [32] proposed a temperature-flowrate based scheduling model that directly adopted temperature and flowrate as decision variables. Although the studies above have significant merits, this scheduling model still specified all performance parameters as constants.…”

Rapid Identification of Economic Indicators of Integrated Energy Systems Based on Data Analysis

“…e planning and design of comprehensive energy supply systems mostly take the economy as the planning goal, supplemented by indicators such as carbon emissions, energy efficiency, and reliability, to arrive at a comprehensive and optimal plan [12]. Wei et al established a nonlinear model considering the configuration of system equipment and optimized operation on the basis of the optimal annual cost and proposed a corresponding evaluation system and evaluation configuration plan [13]. Hung et al proposed two levels of system planning optimization and operation scheduling optimization for the planning model of the integrated energy supply system, constructed a two-level planning mathematical model considering the economy and environmental protection, and used particle swarm optimization as the solution method [14].…”

Integration of biodiesel internal combustion engines and transcritical organic Rankine cycles for waste‐heat recovery in small‐scale applications

“…1,2 In this context, integrated energy systems appear an attractive solution to meet the energy requests of various users, [3][4][5] increase efficiency and flexibility, [6][7][8] and reduce emissions. [9][10][11] Among the available technologies, internal combustion engines (ICE), 12,13 and organic Rankine cycles (ORC) 14,15 are in the spotlight of the industry and research community. 16,17 Particularly, the internal combustion engine is a well-known and reliable technology with the prospect to maintain a preeminent role in the next future.…”