Smart Energy System for Carbon Reduction and Energy Saving: Planning, Operation and Equipments

To enhance the performance of power inspection robots in intricate nuclear power stations, it is necessary to improve their response speed and accuracy. This paper uses the manipulator of the power inspection robot as the primary research object, and unlike previous control algorithm research, which only remained in the software simulation stage, we constructed a set of physical verification platforms based on CAN communication and physically verified the robotic arm’s control algorithm. First, the forward motion model is established based on the geometric structure of the manipulator and D-H parameter method, and the kinematic equation of the manipulator is solved by combining geometric method and algebraic method. Secondly, in order to conduct comparison tests, we designed PID controllers and expert PID controllers by utilising the expertise of experts. The results show that compared with the traditional PID algorithm, the expert PID algorithm has a faster response speed in the control process of the manipulator. It converges quickly in 0.75 s and has a smaller overshoot, with a maximum of only 6.9%. This confirms the expert PID algorithm’s good control effect on the robotic arm, allowing the six-degree-of-freedom robotic arm to travel more accurately and swiftly along the trajectory of the target point.

With the introduction of a high proportion of new energy sources, the power system needs new means to enhance its regulating flexibility. High-energy-consuming industrial loads on the demand side have significant potential to improve the grid’s regulating flexibility. Unplanned power adjustments can affect normal load production. This paper proposes a power control strategy that considers the normal production order of loads, aiming to balance load response capacity requirements and safe production order. Taking copper electrolysis load as an example, based on the load process flow and the power characteristics of production equipment, factors affecting load production order and methods for calculating impact weights are analyzed, and a strategy to reduce the demand response to production order caused by power control is proposed. The effectiveness of the power control strategy is verified through simulation examples, providing a feasible solution for industrial loads to participate in demand-side response.

In order to address the planning problem of integrated energy system (IES) under the goal of “dual-carbon”, this paper proposes a multi-objective planning method for IES with carbon trading mechanism based on CVaR (Conditional Value at Risk). Firstly, this paper establishes the IES energy supply equipment model and the improved stepped carbon trading model. Moreover, this paper proposes the IES multi-objective two-layer planning model based on the consideration of carbon trading cost. The upper layer of the planning model takes the optimization of economy and environmental as the goal to realize the rational planning of the integrated energy system. The lower layer model takes the minimum operating cost as the goal to optimize the system operating conditions and verify the rationality of the planning results. Then, the uncertainty model based on mean-CVaR is established for the uncertainty of carbon trading price and new energy output in the planning process. Finally, this paper sets up cases and solves the model using non-dominated sorting genetic algorithm-II (NSGA-II) and solver, which shows that the proposed method can realize the IES low-carbon planning while guaranteeing the economy.

Concerning energy waste and rational use, this paper studies the optimal scheduling of day-ahead energy supply and the community’s demand with a combined cooling, heating, and power (CCHP) system in summer. From the perspective of bilateral costs and renewable energy use, this paper examines the impact of energy storage systems integrated into cogeneration systems. The Gurobi solver is used to optimize the residential community’s supply and demand sides of the traditional CCHP system (T-CCHP) and the CCHP system with energy storage (CCHP-ESS) under insufficient solar power. Subsequently, two optimal arrangements for energy consumption on the user side under these systems are suggested. In the optimization model, energy storage is added to the T-CCHP system on the energy supply side. On the user side, the energy use scheme is optimized considering the user’s comfort. The innovation point of this study is that the optimization of comprehensive energy in the park involves both supply and demand. The impact of increasing energy storage is discussed on the energy supply side, and the impact of optimization of the energy use plan on costs is discussed on the user side.