• Energy Research

Optimal operation of the battery energy storage system (BESS) is very important to reduce the running cost of a microgrid. Rolling horizon-based scheduling, which updates the optimal decision based on the latest information, is widely applied to microgrid operation. In this paper, the optimal scheduling of a microgrid, considering the energy cost, demand charge, and the battery wear-cost, is formulated as a mixed integer linear programming (MILP) problem. This paper also deals with two practical and important issues when applying the rolling-horizon strategy to BESS scheduling. First, to mitigate the high dependency of the load forecast on the latest information, a confidence weight parameter method is proposed. Second, a new target state of charge (SOC) assignment method is proposed to avoid the depletion of BESS and to reduce the wear-cost of the battery. In addition to the optimal scheduling, a novel real-time control scheme is proposed to mitigate the effect of the forecast uncertainty. The performance of the proposed methods is tested with data measured from a campus microgrid.

As the numbers of microgrids (MGs) and prosumers are increasing, many research efforts are proposing various power sharing schemes for multiple MGs (MMGs). Power sharing between MMGs can reduce the investment and operating costs of MGs. However, since MGs exchange power through distribution lines, this may have an adverse effect on the utility, such as an increase in peak demand, and cause local overcurrent issues. Therefore, this paper proposes a power sharing scheme that is beneficial to both MGs and the utility. This research assumes that in an MG, the energy storage system (ESS) is the major controllable resource. In the proposed power sharing scheme, an MG that sends power should discharge at least as much power from the ESS as the power it sends to other MGs, in order to actually decrease the total system demand. With these assumptions, methods for determining the power sharing schedule are proposed. Firstly, a mixed integer linear programming (MILP)-based centralized approach is proposed. Although this can provide the optimal power sharing solution, in practice, this method is very difficult to apply, due to the large calculation burden. To overcome the significant calculation burden of the centralized optimization method, a new method for determining the power sharing schedule is proposed. In this approach, the amount of power sharing is assumed to be a multiple of a unit amount, and the final power sharing schedule is determined by iteratively finding the best MG pair that exchange this unit amount. Simulation with a five MG scenario is used to test the proposed power sharing scheme and the scheduling algorithm in terms of a reduction in the operating cost of MGs, the peak demand of utility, and the calculation burden. In addition, the interrelationship between power sharing and the system loss is analyzed when MGs exchange power through the utility network.

A new operation method for an energy storage system (ESS) was proposed to reduce the electricity charges of a customer paying the wholesale price and participating in the industrial conservation initiative (ICI) in the Ontario electricity market of Canada. Electricity charges were overviewed and classified into four components: fixed cost, electricity usage cost, peak demand cost, and Ontario peak contribution cost (OPCC). Additionally, the online market data provided by the independent electricity system operator (IESO), which operates the Ontario electricity market, were reviewed. From the reviews, it was identified that (1) the portion of the OPCC in the electricity charges increased continuously, and (2) large errors can sometimes exist in the forecasted data given by the IESO. In order to reflect these, a new schedule-based operation method for the ESS was proposed in this paper. In the proposed method, the operation schedule for the ESS is determined by solving an optimization problem to minimize the electricity charges, where the OPCC is considered and the online market data provided by the IESO is used. The active power reference for the ESS is then calculated from the scheduled output for the current time interval. To reflect the most recent market data, the operation schedule and the active power reference for the ESS are iteratively determined for every five minutes. In addition, in order to cope with the prediction errors, methods to correct the forecasted data for the current time interval and secure the energy reserve are presented. The results obtained from the case study and actual operation at the Penetanguishene microgrid test bed in Ontario are presented to validate the proposed method.

Losses in energy storage systems (ESSs) are considered operational costs and it is critical to improve efficiency in order to expand their use. We proposed a method of improving efficiency through the operation algorithm of an ESS, consisting of multiple energy storage units (ESUs). Since the ESS used for frequency adjustment maintains a fast response characteristics, we have created a lookup table of efficient ESU operating combinations using genetic algorithms in advance to maintain a fast response time. In addition, the charge/discharge energy balance between the plurality of ESUs is maintained, and the deterioration is prevented from being concentrated in some ESUs. The proposed algorithm was reviewed based on three case studies. The first case study examined whether the lookup table obtained using the genetic algorithm well identified the optimal solution. The second compared the system that optimally distributes the output to ESUs to achieve ESS efficiency for frequency regulation with the system that uniformly distributes the output to ESUs. The third analyzed the impact of efficiency enhancement on the operation of an ESS for frequency regulation through a one-month simulation. The findings confirmed that when the entire system output is optimally allocated to the ESUs, the system’s economic efficiency can be improved by reducing the loss while maintaining the accumulative charge/discharge balance.

The voltage and reactive power control (Volt/VAR Control, VVC) in distribution networks has become a challenging issue with the increasing utilization of distributed generations (DGs). In this paper, a heuristic-based coordinated voltage control scheme that considers distribution voltage control devices, i.e., on-load tap changers (OLTC) and step voltage regulators (SVR), as well as reactive power control devices, i.e., DGs, are proposed. Conventional voltage control methods using non-linear node voltage equations require complex computation. In this paper, the formulation of simplified node voltage equations accounting for changes in tap position of distribution voltage control devices and reactive power changes of reactive power control devices are presented. A heuristic coordinated voltage control scheme using the proposed simplified node voltage equations is proposed. A coordinated voltage control scheme to achieve voltage control for nominal voltage and conservative voltage reduction (CVR) is presented. The results of the proposed schemes are compared with the results from the quadratic optimization method to confirm that the proposed schemes yields suitably similar results. Furthermore, a tap scheduling method is proposed to reduce the number of tap changes while controlling network voltage. The tap position is readjusted using a voltage control performance index (PI). Simulation results confirm that when using this method the number of tap changes is reduced. The proposed scheme not only produces reasonable performance in terms of control voltage of networks but also reduces the number of tap changes made by OLTC. The proposed control method is an alternative candidate for a system to be applied to practical distribution networks due to its simplified calculations and robust performance.

In this paper, the VVO (Volt/Var optimization) is proposed using simplified linear equations. For fast computation, the characteristics of voltage control devices in a distribution system are expressed as a simplified linear equation. The voltage control devices are classified according to the characteristics of voltage control and represented as the simplified linear equation. The estimated voltage of distribution networks is represented by the sum of the simplified linear equations for the voltage control devices using the superposition principle. The voltage variation by the reactive power of distributed generations (DGs) can be expressed as the matrix of reactance. The voltage variation of tap changing devices can be linearized into the control area factor. The voltage variation by capacitor banks can also be expressed as the matrix of reactance. The voltage equations expressed as simplified linear equations are formulated by quadratic programming (QP). The variables of voltage control devices are defined, and the objective function is formulated as the QP form. The constraints are set using operating voltage range of distribution networks and the control ranges of each voltage control device. In order to derive the optimal solution, mixed-integer quadratic programming (MIQP), which is a type of mixed-integer nonlinear programming (MINLP), is used. The optimal results and proposed method results are compared by using MATLAB simulation and are confirmed to be close to the optimal solution.

This paper proposes a fault location method for power distribution networks using phasor measurement units (PMU) and short circuit analysis. In order to improve the problems of the existing studies, we focused on several approaches as follows. First, in order to minimize the number of PMU installations, a fault location estimation of lateral feeders through short circuit analysis was presented. Second, unbalanced faults and impacts of photovoltaic (PV) were considered. The proposed method consists of two stages. In Stage 1, the fault location was estimated for the main feeder using PMU installed at the start and end points of the main feeder. Symmetrical components of voltage and current variation were calculated by considering the impact of PVs interconnected to the lateral feeders. If the result of Stage 1 indicated a connection section of lateral feeder on the main feeder, Stage 2 would be performed. In Stage 2, the fault location was estimated for the lateral feeder by comparing the results of the short circuit analysis and measurements of PMUs. The short circuit analysis was based on an unbalanced power flow that considered dynamic characteristics of the PV inverter. The proposed method was verified through various fault situations in a test system. For the applicability of the proposed algorithm to the actual system, a noise test was also performed.