• Energy Research

Demand-side management is a fundamental up-to-date strategy that transforms the traditional power grid to a modern smart grid where the flexible pricing mechanisms play a critical role in its successful implementation. In this paper, the pricing-demand response between a distribution system operator (DSO) and load aggregators (LAs) is modeled as a Stackelberg game, where the DSO is the price maker that adjusts its strategy based on observed responses from LAs. With the concerns of computational cost and privacy protection, two distributed solution approaches, particle swarm optimization and pattern search algorithm, are investigated and compared with the classical centralized backward induction approach. Numerical results on a small case study demonstrate the effectiveness of the proposed distributed solution approaches in leveraging flexible demand response potential.

Distribution-level electricity market provides a platform for trading energy and grid services from large-amount of small-scale distributed energy resources (DERs) located on distribution grids. The behavior of the DERs in the distribution electricity market may ultimately impact the market-clearing and locational marginal prices (LMPs) in the wholesale market. This paper proposes a bi-level optimization model for distribution market clearing and distribution locational marginal pricing (DLMP) considering the interactions between distribution and transmission wholesale markets. In the proposed model, the upper-level model represents the distribution system operator (DSO) market-clearing and the lower-level model represents the wholesale market-clearing by the independent system operator (ISO). The LMP at the substation will impact the DER dispatch and power demands of the DSO as well as the DLMP. In turn, the power demands of the DSO will further impact the ISO market and its LMPs. The equilibrium problem with equilibrium constraints (EPEC) approach is applied to find the equilibria of multiple DSOs and the ISO. The EPEC problem is transformed into a single-level mixed-integer convex problem in order to allow for efficient solving. The effectiveness of the proposed model and solution method are demonstrated through case studies.

This paper proposes an optimization model for the optimal sizing of photovoltaic (PV) and energy storage in an electric vehicle extreme fast charging station considering the coordinated charging strategy of the electric vehicles. The proposed model minimizes the annualized cost of the extreme fast charging station, including investment and maintenance cost of PV and energy storage, cost of purchasing energy from utility and demand charge. The decision variables are capacity of invested PV and the power and energy ratings of invested energy storage. To further reduce the annualized cost of the extreme fast charging station, the charging strategy of electric vehicles are integrated into the optimization model and coordinated with the power output of PV and charging/discharging of energy storage. Results of numerical simulations indicate that investment of PV and energy storage could help reduce the annualized cost of the extreme fast charging station significantly. Meanwhile, the impacts of various parameters on the optimal solution are investigated by sensitivity analysis.

Abstract Solar forecasting has evolved towards becoming a key component of economical realization of high penetration levels of photovoltaic (PV) systems. This paper presents two novel stochastic forecasting models for solar PV by utilizing historical measurement data to outline a short-term high-resolution probabilistic behavior of solar. First, an uncertain basis functions method is used to forecast both solar radiation and PV power. Three possible distributions are considered for the uncertain basis functions - Gaussian, Laplace, and Uniform distributions. Second, stochastic state-space models are applied to characterize the behaviors of solar radiation and PV power output. A filter-based expectation-maximization and Kalman filtering mechanism is employed to recursively estimate the system parameters and state variables. This enables the system to accurately forecast small as well as large fluctuations of the solar signals. The introduced forecasting models are suitable for real-time tertiary dispatch controllers and optimal power controllers. The PV forecasting models are tested using solar radiation and PV power measurement data collected from a 13.5 kW PV panel installed on the rooftop of our laboratory. The results are compared with standard time series forecasting mechanisms and show a substantial improvement in the forecasting accuracy of the total energy produced.

Grid supportive (GS) modes integrated within distributed energy resources (DERs) can improve the frequency response. However, synthesis of GS modes for guaranteed performance is challenging. Moreover, a tool is needed to handle sophisticated specifications from grid codes and protection relays. This paper proposes a model predictive control (MPC)-based mode synthesis methodology, which can accommodate the temporal logic specifications (TLSs). The TLSs allow richer descriptions of control specifications addressing both magnitude and time at the same time. The proposed controller will compute a series of Boolean control signals to synthesize the GS mode of DERs by solving the MPC problem under the normal condition, where the frequency response predicted by a reduced-order model satisfies the defined specifications. Once a sizable disturbance is detected, the pre-calculated signals are applied to the DERs. The proposed synthesis methodology is verified on the full nonlinear model in Simulink. A robust factor is imposed on the specifications to compensate the response mismatch between the reduce-order model and nonlinear model so that the nonlinear response satisfies the required TLS. 2018 IEEE International Symposium on Power Electronics for Distributed Generation Systems (PEDG)

The increasing complexity of power system has made it difficult to simulate large-scale power system in detail. Therefore, to improve computational efficiency, model reduction techniques has been applied to reduce the external area and focus on the dynamics of the internal area. This paper proposes a measurement-based model reduction method which reduces the external system by replacing external system with ARX equivalents. The modeled transfer function parameters are obtained using system identification approach. Case studies on the NPCC system and EI system are carried out. Results show that the proposed reduction method can effectively retain the dynamic behavior of the study area while mitigating computation burden.

Abstract Electric utility companies work to restore as much load as possible after power outages caused by extreme weather events. In this paper, an outage management strategy is proposed to enhance distribution system resilience through network reconfiguration and distributed energy resources (DERs) scheduling. After a line fault, the proposed algorithm can identify radial network topology based on the rank of the incidence matrix. The reconfiguration is implemented by switching tie lines and sectionalizing lines. With the new network topology, an optimal DER scheduling problem is solved to minimize the accumulative cost for dispatchable DER operation and load reduction. Finally, the optimal topology that minimizes the accumulative cost is selected from all radial topologies. The computational workload is relatively low because only linear programming needs to be solved. Using the case studies of the IEEE 69-bus and IEEE 123-bus systems, we consider the worst-case scenarios in which faults occur in the upstream feeder. The simulation results demonstrate that the proposed strategy allows for a relatively high percentage of the load to remain in service after line faults. Furthermore, compared with microgrid-formation approaches, the proposed strategy has advantages when applied to the distribution systems with several normally-open tie lines and low DER penetration.

Renewable energy has been booming globally thanks to its economic, social, and environmental benefits. However, small distributed generation (DG) systems using renewable energy have not yet achieved a significant level of penetration. With deregulation of electricity market, policies have been available to facilitate interconnection of small distributed generators (DGs) with electric grids. However, dispatchability and reliability still present technical barriers for small DGs to play a significant role in the open market, thus limiting their ability to provide value-added services. This paper presents a new concept to enhance the dispatchability of DGs through an aggregated DG network. Dispersed DGs with different energy resources, such as wind turbines, photovoltaics, small hydros, fuel cells, and microturbines, are integrated into a single aggregated generating plant via open power transmission networks. Traditional SCADA dedicated optical fibers, copper and other dedicated wireless physical layers can be replaced by Internet access and low-cost point-to-point wireless communication links to reduce infrastructure costs. The aggregated power generation is balanced between firm DGs and intermittent DGs to allow for the required dispatchability. Day-ahead and hourly generation scheduling can be committed in wholesale electricity trading, thereby achieving the desired added economic benefits. This is usually more favorable than being credited at the avoided cost as seen by utilities with traditional individual DGs.

Interconnected power systems experienced a significant increase in size and complexity. It is computationally burdensome to represent the entire system in detail to conduct power system analysis. Therefore, the model of the study system must be retained in detail while the external system can be reduced using system reduction techniques. This paper proposes a measurement-based dynamic equivalent in order to increase both model accuracy and simulation speed. The proposed method uses a set of measurements at the boundary nodes between the study area and external area for model parameter identification. Case studies demonstrate that the measurement-based technique can capture the main system behaviors accurately and improve computational efficiency.