While reducing the carbon emissions of traditional coal-fired units, carbon capture and storage (CCS) technology can also provide sufficient carbon raw materials for power to gas (P2G) equipment, which helps to achieve the low-carbon dispatch of an integrated energy system (IES). In this paper, an extended carbon emission flow (ECEF) model integrating CCS-P2G coordinated operation and low-carbon characteristics of an energy storage system (ESS) is proposed. On the energy supply side, the coupling relationship between CCS and P2G is established to realize the low-carbon economic operation of P2G. On the energy storage side, the concept of “state of carbon (SOCB)” is introduced to describe the carbon emission characteristics of ESS to exploit the potential of coordinated low-carbon dispatch on both sides of energy release and energy storage. On the load side, a new carbon emission reduction mechanism that guides users to actively respond and reduce carbon emissions is proposed, namely a low-carbon demand response mechanism. This mechanism uses the node carbon intensity as guiding signals, and the willingness of carbon emission reduction or the price in the carbon market as incentive signals. Furthermore, a low-carbon economic dispatch model considering multiple uncertainties such as wind power output, electricity price, and load demands is established. In order to solve the model efficiently, the parallel multi-dimensional approximate dynamic programming (PMADP) algorithm is adopted, and the solution efficiency is significantly improved compared with stochastic optimization, without losing solution accuracy under a multi-layer parallel loop nesting framework. The effectiveness of the proposed model and method is verified on an E14-H6-G6 system and an E57-H12-G12 system.

Finding a global optimal solution to the distribution network reconfiguration (DNR) problem in a short time is a challenging task. This study proposes a real‐time online data‐driven DNR (3DNR) method. Power loss minimisation, lowest bus voltage maximisation and reliability maximisation are taken as objectives. First, in this study, a methodology combining heuristic algorithm and metaheuristic algorithm to solve DNR is proposed. Then a set of data that satisfies the data drive model requirements is obtained. Next, the improved convolution neural network is used to train the data set of DNR. Unlike the state‐of‐art methods, the proposed 3DNR can realise the real‐time online reconfiguration without power flow calculation. The feasibility and effectiveness of the proposed method are demonstrated on IEEE‐34, IEEE‐123, and a practical distribution system in Taiwan.

The conventional consistency-based distributed control methods of microgrids focus on the final state of microgrids and ignore the convergence performance. It is hard for such distributed control schemes to achieve optimal control of microgrids. This paper proposes a distributed secondary optimal control method for islanded DC microgrids to recover voltage and minimize generation cost through one-way point to point communication among the neighbour distributed generations (DGs). A kind of novel observer only using the neighbour DGs information is designed to overcome the unavailability of all information. Applying linear quadratic optimal control theory, the distributed controllers are designed based on the novel observer and are derived by solving a Riccati equation to improve the convergence speed. The distributed controller can effectively achieve the objectives of voltage recovery and generation cost minimization, and the corresponding cost function is asymptotically optimal. The observer gains are effectively selected to ensure the stability of the observer. The feasibility of the proposed control method is demonstrated by simulation under various sources of uncertainty. The comparison results indicates that the proposed controller can achieve voltage recovery and minimize generation cost with a fast convergence speed.

To make an appropriate compromise between the solving speed and solution quality for a distribution network with distributed generations (DGs), an efficient reconfiguration algorithm based on vector shift operation (VSO) considering DGs is proposed. The nodal equivalent power model considering the influence of DGs is established. Power moment is selected as an index to decide whether load flow calculation or VSO should be conducted. In the former case, all bus voltages are updated and then be regarded as the initial condition for VSO. In the latter case, the power and resistance vectors are first formulated based on the structure and parameters of the distribution system, and the variation of power loss after branch exchange is then calculated based on these vectors instead of the time‐consuming load flow calculation. The power and resistance vectors are updated by simple element shift operation after each step of branch exchanging. The final optimal solution is obtained when the open‐branch set remains unchanged after branch exchanging. The proposed VSO‐based reconfiguration algorithm is applied to a practical distribution system in Taiwan. The influences of the DGs and network reconfiguration on power loss and nodal voltages are illustrated. Test results show that the proposed reconfiguration algorithm is feasible and efficient.