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Simultaneous deployment of the electric vehicle charging stations (EVCSs) and distributed photovoltaic stations (DPVSs) in the distribution systems is an effective way to reduce greenhouse gas emissions, promote renewable power adoption, and achieve sustainable development in energy utilization. In this context, how to deploy the EVCSs and DPVSs in the distribution systems with a reasonable scheme is of great importance. In this article, a joint planning model is developed to optimize locations and capacities of the EVCSs and DPVSs simultaneously to reduce energy losses in the distribution systems. In the joint planning model, constraints on bus voltage deviations and line currents are both formulated as chance constraints to ensure that the distribution systems are in reasonable operating statues. To quantify these two chance constraints, a scenario-based method is developed to calculate the probabilistic power flow of the distribution systems during a typical planning day, in which random characters of the DPVS generations and the EVCS charging loads are both considered. The joint planning model of the EVCSs and DPVSs developed in this article is difficult to be solved by mathematical optimization methods. Therefore, genetic algorithm (GA) is customized and utilized to solve the joint planning model of the EVCSs and DPVSs. Finally, a case study based on IEEE 33-bus distribution systems validates the joint planning model and its solving algorithm.

Phase swapping, which rebalances the unbalanced three-phase low voltage (LV, 415 V) networks, improves network efficiency by reducing capacity waste and energy losses. A key challenge against phase swapping is that the majority of LV networks are data scarce, i.e., there is a general lack of data in LV networks. In light of this, this paper proposes a new statistical approach to develop phase swapping guidance for data-scarce LV networks with neither time-series network measurements nor customer metering data. First, given a set of data-rich LV networks (with time-series phase currents data collected at LV substations throughout a year), typical load profiles and their weights in each of the three phases are extracted by applying a nonnegative matrix factorization method. Then, phase swapping guidance are developed for data-rich LV networks along with their rebalancing potentials (rebalancing potentials refer to the reduction of phase imbalance degree). Second, a rapid screening model is developed to efficiently identify the data-scarce LV networks with high rebalancing potentials. Phase swapping guidance are then developed for these data-scarce networks with high rebalancing potentials. Case studies reveal that the statistical approach produces effective phase swapping guidance, which reduce the phase imbalance degrees for 99% of the LV networks and the maximum reduction is 35%. Validation results show that the average reduction of the phase imbalance degree for data-scarce networks is only 14.3% less than that for data-rich networks.

To make fully use of power transmission capacities of the tie-lines and reduce the total operating costs of the power grid, a marginal power generation cost based iterative optimization is proposed to optimize exchange power of the tie-lines. The proposed methodology iteratively optimizes exchange power of the tie-lines, starting from initial operation states of each regional power grid and initial exchange power of each tie-line without changing the results of unit commitment. The method can be easily applicated to engineering practice because it is closely related to the actual dispatching process of the power grid, and only requires information of the marginal unit operation in each regional power grid, protecting the data privacy of each regional power grid. Finally, a simulation is performed based on a case system of three regional grids and three tie-lines, and the simulation results verify the method.

Battery energy storage systems (BESSs) are incorporated into wind farms to gain more profits by shifting energy over time and to track predetermined power schedules. In operations, charging/discharging power of the BESS is adjusted flexibly to follow the power schedules of the wind-BESS hybrid systems (W-BESS-HS), which are set to be the sum of short-term predicted wind powers and charging/discharging schedules of the BESS. In order to extend lifetime of batteries, the BESS operation is subject to a sequential charging/discharging state sequence, which is predetermined according to time-of-use (ToU) pricing schemes. An iteration scheme is presented to update scheduled charging/discharging rates of the BESS according to simulation results based on sequential Monte-Carlo simulation (SMCS) technology so that the W-BESS-HS can not only meet a probabilistic requirement on generation schedule tracking but also gain further economic benefits by achieving a trade-off between punishments resulted from power deviations and wind power curtailment losses. In the SMCS simulation, a series of real-time indices are presented to evaluate performances of the W-BESS-HS at every dispatching interval and provide updating directions of the iteration scheme. The research work can provide theoretical support when operating the W-BESS-HS in ToU pricing schemes.

In traditional optimal dispatch of combined cooling, heating and power (CCHP) microgrid, constraint on heat-power balance is considered, but neglecting thermal characteristics of heating network, which reduce the economy and practice value of the dispatching results. In this paper, thermal characteristics of heating network are modeled on the basis of transmission delay and temperature loss pertaining to pipelines. Then, a dispatch optimization model aiming to minimize daily operation costs of the CCHP microgrid is developed, in which constraints on load balance, equipment safety and thermal characteristics are considered. The optimization is a mixed integer non-linear programming problem that can be solved by GAMS. Simulation results demonstrate that the dispatching model proposed in this paper can fully exploit thermal storage capacity of heating network and change the profile of heat load, and thereby deliver more economical and practical dispatching solution. Therefore, thermal characteristics of heating network must be taken into account when dispatching a CCHP microgrid.

The outputs of adjacent wind farms have some correlation characteristics because of similar resource conditions, which has certain influences on transmission network expansion planning. In this paper, the influences of multi-wind power output correlations on transmission network expansion planning are considered. Firstly, the correlations between the outputs of adjacent wind farms are studied by Copula theory, and the method of generating wind farm samples with correlation is presented. Secondly, a transmission network expansion planning model is constructed based on the minimum cost method. In this model, Monte Carlo simulation technology is utilized to analyze uncertain characteristics of power flows resulted from multi-wind farms that are integrated into electric power grids simultaneously. At last, the simulation results based on the improved Garver 6 system verify the feasibility and rationality of the proposed model and algorithm. The results also show that the correlations of wind power outputs among multi-wind farm have a significant effect on the transmission network planning results, which should be taken into account in the planning.