Electric Power Output Optimization for CCHP Using PSO Theory

Fu, Xueqian; Huang, Shangyuan; Li, Rui; Sun, Hongbin

doi:10.1016/j.egypro.2016.11.241

Cited by 12 publications

(4 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The game theoretic optimal scheduling model of the multi-energy hub system is established according to Equations (6), (10a), (10b), (11), (13)- (16). In the multi-energy hub system, the scheduling decision of the energy hub is affected by other decision makers; as such, game theory is advantageous for decision making in the optimal scheduling problem of the multi-energy hub system.…”

Section: Equations Of the Game Theoretic Optimal Scheduling Model Amomentioning

confidence: 99%

“…With respect to solving the optimal scheduling model, particle the swarm optimization method is used to calculate the output of Combined Cold, Heat and Power (CCHP) units [16]. A hybrid optimization method based on the genetic algorithm (GA) and a nonlinear interior point method (IPM) is utilized to solve the optimal day-ahead scheduling model for an integrated urban energy system [17].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Optimal Scheduling Method for Multi-Energy Hub Systems Using Game Theory

et al. 2019

View full text Add to dashboard Cite

The optimal scheduling of multi-energy hub systems plays an important role in the safety, stability, and economic operation of the system. However, due to the strong uncertainty of renewable energy access, serious coupling, and the interaction among energy hubs of multi-energy hub systems, it is difficult for the traditional optimal scheduling method to solve these problems. Therefore, game theory was used to solve the optimal scheduling problem of multi-energy hub systems. According to the internal connection mode and energy conversion relationship of energy hubs, along with the competitive and cooperative relationship between multi-energy hubs, the game theoretic optimal scheduling model of the multi-energy hub system was established. Then, two cases and 50 groups of wind speed series were used to test the robustness of the proposed method. Simulation results show that the total power injection is −16,805.8, 104.1847, and −865.561 and the natural gas injection is 46,046.81, 27,727.65, and 63,039.54 in spring/autumn, summer, and winter, respectively, which is consistent with the characteristics of the four seasons. Furthermore, the optimal scheduling method using game theory has a strong robustness in multi-energy hub systems.

show abstract

Section: Equations Of the Game Theoretic Optimal Scheduling Model Amomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Optimal Scheduling Method for Multi-Energy Hub Systems Using Game Theory

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Wang et al [10] demonstrated that a multi-objective optimization will not give one single solution but rather a set of Pareto optimal solutions. Often, the objectives are conflicting and different approaches to solve the minimization problem exist, e.g., mixed integer linear programming with weighted sums [11], evolutionary algorithms [12], game theory [13], particle swarm optimization [14], genetic algorithms [15], etc.…”

Section: Introductionmentioning

confidence: 99%

Cost-Effectiveness of Carbon Emission Abatement Strategies for a Local Multi-Energy System—A Case Study of Chalmers University of Technology Campus

et al. 2020

View full text Add to dashboard Cite

This paper investigates the cost-effectiveness of operation strategies which can be used to abate CO 2 emissions in a local multi-energy system. A case study is carried out using data from a real energy system that integrates district heating, district cooling, and electricity networks at Chalmers University of Technology. Operation strategies are developed using a mixed integer linear programming multi-objective optimization model with a short foresight rolling horizon and a year of data. The cost-effectiveness of different strategies is evaluated across different carbon prices. The results provide insights into developing abatement strategies for local multi-energy systems that could be used by utilities, building owners, and authorities. The optimized abatement strategies include: increased usage of biomass boilers, substitution of district heating and absorption chillers with heat pumps, and higher utilization of storage units. The results show that, by utilizing all the strategies, a 20.8% emission reduction can be achieved with a 2.2% cost increase for the campus area. The emission abatement cost of all strategies is 36.6–100.2 (€/tCO 2 ), which is aligned with estimated carbon prices if the Paris agreement target is to be achieved. It is higher, however, than average European Emission Trading System prices and Sweden’s carbon tax in 2019.

show abstract

“…Other related studies include [16,[20][21][22][23][24]. However, since mathematical programming methods may face difficulty in some complex optimization problems, intelligent algorithms such as particle swarm algorithm (PSO) [25,26] and genetic algorithm (GA) [27] have also been used. For instance, Wang et al [25,28] employed PSO and GA to find the optimal configuration of a CCHP system such that the system cost is minimized.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimal Design of Renewable Energy Integrated CCHP System Using PICEA-g

Wang

Zhang

et al. 2018

Energies

View full text Add to dashboard Cite

The integration of renewable energies into combined cooling, heating, and power (CCHP) systems has become increasingly popular in recent years. However, the optimization of renewable energies integrated CCHP (RECCHP) systems (i.e., optimal component configurations) is far from being well addressed, especially in isolated mode. This study aims to fill this research gap. A multi-objective optimization model characterizing the system reliability, system cost, and environmental sustainability is constructed. In this model, the objectives include minimization of annual total cost (ATC), carbon dioxide emission (CDE), and loss of energy supply probability (LESP). The decision variables representing the configuration of the RECCHP system include the number of photovoltaic (PV) panels and wind turbines (WTs), the tilt angle of PV panels, the height of WTs, the maximum fuel consumption, and the capacity of battery and heat storage tanks (HSTs). The multi-objective model is solved by a multi-objective evolutionary algorithm, namely, the preference-inspired coevolutionary algorithm (PICEA-g), resulting in a set of Pareto optimal (trade-off) solutions. Then, a decision-making process is demonstrated, selecting a preferred solution amongst those trade-off solutions by further considering the decision-maker preferences. Furthermore, on the optimization of the RECCHP system, operational strategies (i.e., following electric load, FEL, and following thermal load, FTL) are considered, respectively. Experimental results show that the FEL and FTL strategies lead to different optimal configurations. In general, the FTL is recommended in summer and winter, while the FEL is more suitable for spring and autumn. Compared with traditional energy systems, RECCHP has better economic and environmental advantages.

show abstract

Electric Power Output Optimization for CCHP Using PSO Theory

Cited by 12 publications

References 7 publications

An Optimal Scheduling Method for Multi-Energy Hub Systems Using Game Theory

An Optimal Scheduling Method for Multi-Energy Hub Systems Using Game Theory

Cost-Effectiveness of Carbon Emission Abatement Strategies for a Local Multi-Energy System—A Case Study of Chalmers University of Technology Campus

Multi-Objective Optimal Design of Renewable Energy Integrated CCHP System Using PICEA-g

Contact Info

Product

Resources

About