This paper presents a multiobjective optimization method to jointly optimize the planning and operation of a grid-tied microgrid (MG) with various distributed generation sources such as wind turbine and photovoltaic arrays with the assistance of demand side management. In order to explore the economy and demand variety, the problem is formulated as a double-objective optimization to minimize the total annual cost and to maximize the customer satisfaction. To solve the multiobjective optimization problem, a fuzzy satisfaction-maximizing method is adopted to convert the original problem into a single objective optimization problem and a mixed integer linear programming algorithm is then used to solve the problem. To verify the proposed solution, various case studies have been carried out and compared. The results show that the proposed method is effective in minimizing the cost of an MG without sacrificing the satisfaction of customers.

A novel two-stage sequential disaster recovery strategy for resilient cyber-physical distribution power systems (CPDPSs) is proposed with consideration of cyber-physical collaborative optimization. In the first stage, a mixed-integer linear programming (MILP) model is formulated based on the integration of a transportation network (TN), a cyber network (CN) and a physical network (PN) to achieve efficient load recovery. Additionally, a ‘repair-event-triggered’ mechanism is proposed to facilitate effective and collaborative optimization among physical maintenance crews (PMCs), cyber maintenance crews (CMCs), and a distribution network recovery model (DNRM). In the second stage, PMCs and CMCs are rescheduled to repair the remaining damaged components with the lowest time cost after all the power supplies are restored. The effectiveness and applicability of this proposed model are validated using the IEEE 33 bus system and the PG&E 69 bus system.

The influences of network reconfiguration and distributed generations (DGs) on distribution system state estimation (DSSE) and measurement placement should not be ignored in active distribution systems. In this paper, considering network reconfiguration and the output uncertainties of DGs, a robust measurement placement method for active distribution systems is proposed based on measurement saturation analysis and heuristic algorithm. First, the saturation number determined by measurement saturation characteristic is chosen as the measurement number. Then, the impacts of different network topologies are represented by various weights in the robust measurement placement model, which are computed by Markov chain and analytic hierarchy process. In addition, Gaussian mixture model is applied to approximate the power fluctuations of DGs. Uncertainties caused by measurements are considered by Monte Carlo simulations. The accuracy of DSSE in different network topologies can be guaranteed by the proposed robust measurement placement method. Simulation results based on the IEEE 33-bus and 119-bus distribution systems demonstrate the effectiveness of the proposed method.

With the increasing contribution of intermittent renewable generations to electric power grids, additional frequency reserves are required to manage the balance between generation and load in power systems. This paper describes the potential contribution of LED lighting loads as frequency reserves to provide ancillary services and proposes a decentralized control strategy for LED lighting loads in response to frequency deviations. Three control modes are developed according to the magnitude and duration of the frequency deviations. Experimental results verify the effectiveness of the use of an illumination controller to regulate illumination with the corresponding control modes. Simulation results demonstrate the validity of using LED lighting loads as frequency reserves to provide frequency regulation. The frequency deviations can be appropriately mitigated using the controlled LED lighting loads.

Several parallel restored subsystems exist in the last stage of the build-up restoration process of bulk power systems. The whole system restoration is completed when these subsystems are re-connected and their internal loads are recovered. This paper pro-poses a robust distributed coordination scheme to achieve reconnection and load recovery of parallel restored subsystems in wind power penetrated transmission systems. First, robust distributed restoration models are constructed considering individual subsystems under uncertain conditions. Then, an inner-outer nested upper-lower (IOUL) iterative algorithm is developed to solve the constructed models. The robust distributed coordination can be realized according to convergence. The proposed algorithm solves the complex non-convex model with uncertain variables by iteratively solving the small-scale mixed-integer linear programming (MILP) problem. The robust distributed restoration scheme allows independent decision-making within subsystems and ensures the feasibility of the distributed restoration strategy under uncertain conditions. The effectiveness of the proposed method is validated using three interconnected 6-bus systems and the IEEE 118-bus system, showing improved computational efficiency and restoration acceleration.

Most of the current wind power interval forecast methods are based on the assumption the point forecast error is subject to a known distribution (such as a normal distribution, beta distribution, etc.). The interval forecast of wind power is obtained after solving the confidence interval of the known distribution. However, this assumption does not reflect the truth because the distribution of error is random and does not necessary obey any known distribution. Moreover, the current method for calculating the confidence interval is only good for a known distribution. Therefore, those interval forecast methods cannot be applied generally, and the forecast quality is not good. In this paper, a general method is proposed to determine the optimal interval forecast of wind power. Firstly, the distribution of the point forecast error is found by using the non-parametric Parzen window estimation method which is suitable for the distribution of an arbitrary shape. Secondly, an optimal method is used to find the minimum confidence interval of arbitrary distribution. Finally the optimal forecast interval is obtained. Simulation results indicate that this method is not only generally applicable, but also has a better comprehensive evaluation index.