In this paper, a cost-benefit analysis based optimal planning model of battery energy storage system (BESS) in active distribution system (ADS) is established considering a new BESS operation strategy. Reliability improvement benefit of BESS is considered and a numerical calculation method based on expectation is proposed for simple and convenient calculation of system reliability improvement with BESS in planning phase. Decision variables include both configuration variables and operation strategy control variables. In order to prevent the interaction between two types of variables and enhance global search ability, intelligent single particle optimizer (ISPO) is adopted to optimize this model. Case studies on a modified IEEE benchmark system verified the performance of the proposed operation strategy and optimal planning model of BESS.

To enable an in-depth study of active cyber-physical distribution network, a cyber subsystem model is urgently needed to describe the performance in distribution communication. The methods for quantifying the interactions between subsystems, especially indirect interactions, have not been adequately studied in the existing research. In this paper, a novel model is developed to evaluate the validity of cyber link considering dynamic routing, delay, and communication error, particularly the cyber traffic. Then, an analytical method is presented to quantify the impact of cyber faults considering the functionality validity during distribution automation. And the reliability of cyber and physical subsystems is evaluated based on Non-Sequential and Sequential Monte Carlo methods respectively. Finally, a test system for reliability evaluation is established to analyze the influences of cyber faults. Also sensitivity analyses on the impact of cyber network traffic, element failure rate, and network topology and access communication technology are carried out. The obtained results could provide useful insights into planning and operation of active cyber-physical distribution networks.

Soft normally open points (SNOPs) connected to distribution networks are instrumental in maintaining uninterrupted power supply and improving power quality during the fault period. In order to achieve the optimal service restoration scheme, a novel bi-level service restoration model based on the robust optimization method is proposed in this paper. The upper level of the proposed model aims at minimizing the risk of load loss. The switch statuses and range of power transmitted by SNOP terminals can be obtained. In the lower level of the model, the interval robust optimization method is adopted to cope with the uncertainties of DGs and loads. If there are no optimal solutions, the upper level model needs to be solved once more to adjust the network topology. Finally, a test system is established to verify the effectiveness of the proposed model and solution method. The effects of forecasting errors, the DG penetration, and the ratio of single terminal capacity of SNOP to line capacity on the optimization results are analyzed.

Synergy among renewable energy sources (RESs), energy network (EN) expansion and electrified transport provides a potential pathway toward low-carbon energy systems. However, the ambition of deep decarbonization may not be achieved by connecting all stakeholders through single market-wise effort. To this end, this article presents a coordinative planning framework of public transport electrification (PTE), RESs and ENs for decarbonization of urban multi-energy systems (UMESs). The framework is formulated as a three-level programming and driven by both government incentives and market signals. In the upper-level, the investment decision models of RESs, ENs and PTE are developed. Particularly, public transport hubs (PTHs) are viewed as flexible multi-energy demands whose potential of implementing demand response is incorporated in PTE planning model. In the middle-level, a carbon emission-security assessment subproblem is proposed, where market equilibriums are captured by Karush-Kuhn-Tucker optimality conditions; then the nested Benders decomposition and Lagrangian relaxation approach are utilized to synthetize capacity incentives paid by governments. In the lower-level, a hybrid energy pricing mechanism is designed to obtain integrated energy-carbon prices. The above hierarchical model is solved by an iterative algorithm where all investors update their planning decisions in a decentralized manner based on customized government incentives and market prices. Numerical studies prove that our proposed approach can effectively coordinate all stakeholders and promote the low-carbon transition of UMESs.

As the number and capacity of photovoltaic (PV) power stations increase, it is of great significance to evaluate the PV-connected power systems in an effective, reasonable, and quick way. In order to overcome the challenge of PV’s time-sequential characteristic and improve upon the computational efficiency, this paper presents a new methodology to evaluate the reliability of the power system with photovoltaic power stations, which combines intelligent state space reduction and a pseudo-sequential Monte Carlo simulation (PMCS). First, a non-aggregate Markov model of photovoltaic output is established, which effectively retains some time-sequential representation of the PV output. Then, the differential evolution algorithm (DE) is introduced into the sampling stage of PMCS to carry out an intelligent state space reduction (ISSR). By using the DE algorithm, success states are searched out and removed, thus the state space is reduced and formed with a high density of loss-of-load. Hence, unnecessary samplings are avoided, which optimizes the PMCS sampling mechanism and improves the computational efficiency. Finally, the proposed method is tested in the modified IEEE RTS-79 system. The results indicate that this new method has a better computational efficiency than the time-sequential Monte Carlo simulation method (TMCS) and pure PMCS. In addition, the effectiveness and feasibility of this method are also verified.

This paper proposes a cyber-physical active distribution system (CPADS) planning method, which considers the economic aspect and overall performance in the context of distributed generations (DGs) interconnection. First, the composition of CPADS is introduced for analyzing the effect of cyber system on the planning goals and constraints, based on which the dependence of the physical system on the cyber system is investigated. Then, the framework of CPADS planning is proposed by comparing it with active distribution system (ADS). Second, the planning is modeled to search for the appropriate DGs capacities, communication mode, and voltage control frequency with the constraints including performance, construction investment, and out-of-limit voltage probability, aiming at minimizing the construction and operation cost of cyber-physical systems and maximizing the DG penetration level. After that, the bi-layer multi-objective programming without considering the random failure of cyber system is adopted to derive the optimal solution set. Monte Carlo Simulation is then performed to solve for the out-of-limit voltage probability due to cyber failures, based on which the final scheme is obtained. Simulations are conducted on the modified IEEE 33-node distribution system. The results verify the effectiveness of the proposed method.