Multi-objective Operation Strategy Research Articles

Assessing the effectiveness of a residential-scale detention tank operated in a multi-objective approach using SWMM.

The volume capture ratio of annual rainfall (VCRAR) of low-impact development measures is significantly influenced by its operating characteristics, particularly for residential stormwater detention tanks (SWDTs). The multi-objective operation strategy of SWDTs, encompassing toilet flushing (TF), green space irrigation (GSI), combined TF and GSI (TF-GSI), and peak flow reduction (PFR) rate, were compared using a case study in Beijing based on the stormwater management model. The findings indicate that the VCRAR for TF, GSI, and TF-GSI rainwater harvesting targets was 89.05, 77.16, and 91.21%, respectively. The operating scheme and return periods have a significant impact on the PFR rate's effectiveness. When the return period was lower than 10 years, the SWDT does not reach its maximum storage capacity, and the PFR rate was increased with increasing the return period: the PFR rate was 71.47% when the design return period was 10 years. It will also produce the phenomena of water inrush, and the overflow volume will grow rapidly when the SWDT reaches its maximum storage capacity. Hence, the operation of SWDTs may be integrated with real-time control to optimize the VCRAR for rainwater reuse and flood migration, thereby enhancing the volume utilization efficiency of SWDTs.

Multi-Objective Operation Strategy for a Community with RESs, Fuel Cell EVs and Hydrogen Energy System Considering Demand Response

Nowadays, it can be said that loads in the residential and transportation sector constitute an important part of global energy consumption. In this respect, due to environmental problems and in terms of ensuring energy reliability, it is seen that the energy demand is moving towards supplying it from on-site, renewable energy sources (RESs) and in a small-scale region, namely community. In this context, in this study, a multi-objective stochastic optimum energy management model for a renewable-supported hydrogen-based community is proposed with mixed-integer linear programming method. In the structure, the hydrogen energy system (HES) consisting of an electrolyzer, hydrogen tank, and fuel cell provides energy to the community and fuel support for hydrogen fuel cell electric vehicles (HFCEVs). In addition, a demand response (DR) program is implemented, where the room temperatures of the houses are remotely controlled. While electricity can be purchased from the power grid, hydrogen can be traded with the main hydrogen gas network. Besides, renewable spillage and carbon emission costs are considered. Various case studies are conducted using real data from Spain to demonstrate the effectiveness of the presented model. According to the results obtained, the proposed method can reduce the cost of the community by 58.67%.

A multi-objective operation strategy optimization for ice storage systems based on decentralized control structure

Improving the operational efficiency of chillers and science-based planning the cooling load distribution between the chillers and ice tank are core issues to achieve low-cost and energy-saving operations of ice storage air-conditioning systems. In view of the problems existing in centralized control architecture applied in heating, ventilation, and air conditioning, a distributed multi-objective particle swarm optimization improved by differential evolution algorithm based on a decentralized control structure was proposed. The energy consumption, operating cost, and energy loss were taken as the objectives to solve the chiller’s hourly partial load ratio and the cooling ratio of ice tank. A large-scale shopping mall in Xi’an was used as a case study. The results show that the proposed algorithm was efficient and provided significantly higher energy-savings than the traditional control strategy and particle swarm optimization algorithm, which has the advantages of good convergence, high stability, strong robustness, and high accuracy. Practical application: The end equipment of the electromechanical system is the basic component through the building operation. Based on this characteristic, taken electromechanical equipment as the computing unit, this paper proposes a distributed multi-objective optimization control strategy. In order to fully explore the economic and energy-saving effect of ice storage system, the optimization algorithm solves the chillers operation status and the load distribution. The improved optimization algorithm ensures the diversity of particles, gains fast optimization speed and higher accuracy, and also provides a better economic and energy-saving operation strategy for ice storage air-conditioning projects.

Study on Multi-Objective Operation Strategy for Multi-Reservoirs in Small-Scale Watershed Considering Ecological Flows

The operation of multi-reservoir system has significantly influence on ecological system in riverine by altering natural streamflow. To alleviate the negative impacts on the ecosystem in a small-scale watershed, ecological flow requirements (EFRs) are incorporated in the operating strategies to tradeoff between the existing human needs and EFRs. In this study, three scenarios of EFRs are firstly generated to study the impacts on the operation efficiency of the existing objectives. Then, the reservoirs are classified into three categories, and the operation strategies are defined, respectively. Finally, the operation strategies are optimized using NSGA-II algorithm and used to simulate the operation processes. The simulation results indicate that the operation objectives between the EFRs and agricultural irrigation have a significant competitive relationship, and the guarantee rate for agricultural irrigation decreases with the EFRs increasing. The reservoirs in the third category with large storage capacity have strong regulation ability for the EFRs, however the reservoir operations in other categories are more vulnerable in term of guarantee rate for irrigation.

Multi-objective optimization of operation of lignocellulosic acetone-butanol-ethanol fermentation with ex situ butanol recovery (ESBR)

This study proposes a multi-objective operation strategy for a continuous acetone-butanol-ethanol (ABE) fermentation process with ex situ recovery by adsorption (termed “lignocellulosic ESBR-by-adsorption system”), which is used to produce biobutanol from lignocellulosic biomass. In this study, a model-based cyclic steady state (CSS) optimization of the lignocellulosic ESBR-by-adsorption system is performed to find the best operating conditions and gain better understandings of its dynamic behavior. To gain insights into the relationships between operating conditions and performance of the system, key operating variables are optimized with respect to three different objective functions: maximizing productivity, maximizing yield, and minimizing substrate loss. In addition, an optimization study is conducted for two different types of feedstocks to investigate the effect of feedstock type on system performance. Furthermore, an open-loop sensitivity analysis is performed to identify the most influential parameters and operating conditions that provide more stable system behavior when uncertainties are present.

Optimal Operation for Economic and Exergetic Objectives of a Multiple Energy Carrier System Considering Demand Response Program

An MECS (multiple energy carrier system) could meet diverse energy needs owing to the integration of different energy carriers, while the distinction of quality of different energy resources should be taken into account during the operation stage, in addition the economic principle. Hence, in this paper, the concept of exergy is adopted to evaluate each energy carrier, and an economic–exergetic optimal scheduling model is formulated into a mixed integer linear programming (MILP) problem with the implementation of a real-time pricing (RTP)-based demand response (DR) program. Moreover, a multi-objective (MO) operation strategy is applied to this scheduling model, which is divided into two parts. First, the ε-constraint method is employed to cope with the MILP problem to obtain the Pareto front by using the state-of-the-art CPLEX solver under the General Algebraic Modeling System (GAMS) environment. Then, a preferred solution selection strategy is introduced to make a trade-off between the economic and exergetic objectives. A test system is investigated on a typical summer day, and the optimal dispatch results are compared to validate the effectiveness of the proposed model and MO operation strategy with and without DR. It is concluded that the MECS operator could more rationally allocate different energy carriers and decrease energy cost and exergy input simultaneously with the consideration of the DR scheme.

Predictive Multi-Objective Operation Strategy Considering Battery Cycle Aging for Hybrid Electric Vehicles

Predictive Multi-Objective Operation Strategy Considering Battery Cycle Aging for Hybrid Electric Vehicles