Day-Ahead Intelligent Energy Management Strategy for Manufacturing Load Participating in Demand Response

Abstract

Flexible resources such as adjustable load widely participate in interaction with power grid, which can effectively promote renewable energy consumption. In previous studies, researchers generally focused on industrial and household users, but usually ignored the manufacturing load. Therefore, in this paper, an day-ahead intelligent energy management strategy for manufacturing load is proposed. Firstly, we analyze the power demand behavior of manufacturing load in detail, and describe the energy flow and material flow of manufacturing load through state task network (STN) method and mixed integer linear programming model. Then, the conditional deep convolution generative adversarial networks (C-DCGAN) algorithm is used to describe the uncertainty of new energy and construct a set of scheduling scenarios. Finally, case study shows that the proposed method can effectively improve the regional renewable energy consumption level and economic benefits of enterprises. INDEX TERMSManufacturing load; state task network (STN) method; conditional deep convolution generative adversarial networks (C-DCGAN) algorithm; typical scenario screening method

Demand Management for Manufacturing Loads Considering Temperature Control under Dynamic Electricity Prices

“…In order to ensure the optimality of the decision, the MPC method is used for rolling optimization scheduling. An MPC temperature rolling optimization control model for air source heat pumps can be established, as shown in (23):…”

“…However, when there are many air-source heat pumps, the solution may be extremely time-consuming. In order to accelerate the progress of problem-solving, the Formula (24) for finding the optimal solution is derived according to the optimal control law, the equality constraints in (23) are substituted into the objective function, and the control variables are separated to obtain the functional expression of the optimal solution, as shown in Equation (24):…”

“…Detailed production data and corresponding STN diagrams of manufacturing enterprises can be referred to in Ref. [23], and the dynamic electricity price used in this paper is given in Figure 2.…”