A double-deck deep reinforcement learning-based energy dispatch strategy for an integrated electricity and district heating system embedded with thermal inertial and operational flexibility

With the high penetration of wind power connected to the integrated electricity and district heating systems (IEDHSs), wind power curtailment still inevitably occurs in the traditional IEDHS dispatch. Focusing on the flexibilities of the IEDHS is considered to be a beneficial solution to further promote the integration of wind power. In the district heating network, the thermal inertia is utilized to improve such flexibility. Therefore, an IEDHS dispatch model considering the thermal inertia of district heating network and operational flexibility of generators is proposed in this paper. In addition, to avoid the tendency of traditional reinforcement learning (RL) to fall into local optimality when solving high-dimensional problems, a double-deck deep RL (D3RL) framework is proposed in this study. D3RL combines with a deep deterministic policy gradient (DDPG) agent in the upper level and a conventional optimization solver in the lower level to simplify the action and reward design. In the simulation, the proposed model considering the transmission time delay characteristics of the district heating network and the operational flexibility of generators is verified in four scheduling scenarios. Besides, the superiority of the proposed D3RL method is validated in a larger IEDHS. Numerical results show that the considered scheduling model can use the heat storage characteristics of heating pipelines, reduce operating costs, improve the operational flexibility and encourage wind power utilization. Compared with traditional RL, the proposed optimization method can improve its training speed and convergence performance.Ministry of Education (MOE)Nanyang Technological UniversityPublished versionThis work was supported by the School of Electrical and Electronic Engineering at Nanyang Technological University, Ministry of Education, Singapore, under Grant AcRF TIER 1 RG50/21

Low-frequency voltage ripples in the flying capacitors of the nested neutral-point-clamped converter

The flying capacitors (FCs) of the nested neutral-point-clamped (NNPC) converter show an inherent voltage ripple at fundamental frequency. This ripple can be significantly large under some operating conditions of the converter. In this paper, the amplitudes of the low-frequency voltage ripples in the FCs are determined. An averaged model of the NNPC converter is introduced and used in the analysis. The amplitudes of the capacitor voltage ripples are provided using normalized variables so that this information can be used to size the FCs of the converter in different applications. The results of the analysis are validated experimentally in a laboratory prototype

Reliability Worth Analysis of Distribution Systems Using Cascade Correlation Neural Networks

Reliability worth analysis is of great importance in the area of distribution network planning and operation. The reliability worth’s precision can be affected greatly by the customer interruption cost model used. The choice of the cost models can change system and load point reliability indices. In this study, a cascade correlation neural network is adopted to further develop two cost models comprising a probabilistic distribution model and an average or aggregate model. A contingency-based analytical technique is adopted to conduct the reliability worth analysis. Furthermore, the possible effects of adding distributed generation units into the network are evaluated. The proposed approach has been tested on a radial distribution test network evaluating the reliability worth. The results show that the probabilistic distribution model provides a more realistic model for the reliability analysis

Power balance modes and dynamic grid power flow in solar PV and battery storage experimental DC-link microgrid

In this paper, an energy management system, based on different power balance modes and dynamic grid power flow, is proposed to operate a DC-link microgrid based on a solar photovoltaic generator and battery storage, with the option to request variable power from the grid to meet the load demand. The energy management provides the required references, for each mode, based on the solar source availability, the battery status, the power losses, and the grid billing rate. A fuzzy logic system is developed to provide a dynamic grid power flow based on the grid price. Eight power balance modes are defined based on the power generation, storage, and grid affordability to meet the load demand. The objectives are to minimize the energy cost and increase the lifespan of the storage device. The microgrid is controlled to maintain a constant DC-link voltage and regulate the battery current depending on the mode of operation. The proposed energy management system, based on the power balance modes, is experimentally validated on a laboratory-scale DC-link microgrid for different conditions. The experimental results have shown the satisfactory performance of the microgrid and smooth transitions between the different power balance modes.Published versio