Introduction: With the rapid development of the global economy in recent years, the need to meet the demand for load during peak hours has increased. Given the structure of power networks, there will be a kind of inflexibility in responding quickly to load needs. This will show itself more particularly during peak hours. Therefore, in order to deal with the phenomenon of peak load and peak shaving, we can use the methods of scheduling and optimizing the economic load dispatch of power plants that produce electricity and heat at the same time and use energy storage devices. On the other hand, large-scale energy storage technologies will play a key role in energy management. In this paper, the scheduling and optimization of simultaneous generation of power and heat, including the supply of electrical and thermal power required by the network has be implemented. This goal was achieved through two general approaches, including considering the effect of steam valves and examining this effect along with the use of battery-type energy storage during peak load, which is one of the main innovations of the research.
 
Materials and methods: Considering the non-convex and non-linear nature of the problem of optimizing the simultaneous generation of power and heat, including the supply of electrical and thermal power required by the network, for the first time, the Hyper Spherical Search (HSS) algorithm was used to solve this problem. Also, Particle Swarm Optimization (PSO) algorithm was used to compare and validate the results.
 
Results: The simulations were performed on a large-scale 48-unit sample system with many local optimal points. They were performed in two stages. One included economic load dispatch considering the effect of opening the steam valve, and the other considered the peak load model with the presence of battery-type energy storage, which was performed with the aim of calculating the cost to achieve an electrical capacity of 4700 MW and the thermal capacity of 2500 MW. The use of batteries can be applied by the power generation units in two different ways; one included providing 800 MW of the power required by the batteries and the rest of the requested power, which is 3900 MW by the Electric power generation units. The other was injecting 800 MW d into the main network power, which is equal to 5500 MW of the final power delivered to the network.
 
Discussion and Conclusion: In this paper, Combined Heat-and-Power Economic Dispatch (CHP-ED) was first investigated, despite network losses, the effect of opening steam valves, and the peak load by the HSS algorithm. Then, all steps were performed by PSO algorithm. All simulations were performed in MATLAB software. The studied system is a 48-unit power plant for simultaneous production of power and heat: including 26 units for electricity generation; 12 units for cogeneration; and 10 units for heat production, whose complete specifications such as parameters and coefficients of cost functions of units, production range of units of electricity generation, the parameters of heat production units, and the parameters of cogeneration units have been presented by Meng et al. 2015.
After entering the objective functions and related constraints in the program of HSS and PSO algorithms in MATLAB software, the program was executed with 200 iterations. The results obtained from the production power of each unit were separately obtained by HSS and PSO algorithms considering the effect of opening the steam valve. Also, the convergence characteristics of these two methods were presented in the text of the paper.
The Comparison of the results of the two algorithms has demonstrated that the PSO algorithm has better conditions than the HSS algorithm in terms of time to reach the final solution and in terms of optimal fuel cost. However, the total power losses calculated in the HSS algorithm has had better conditions than those in the PSO algorithm.