Guangya Yang

A Decentralized Storage Strategy for Residential Feeders With Photovoltaics

This paper proposes a decentralized storage strategy to support voltage control in low-voltage (LV) residential feeders with high photovoltaic (PV) capacity installed. The proposed strategy is capable of preventing overvoltage situations during high PV generation periods, by the use of locally controlled battery energy storage systems (ESS) at the PV system grid interface. The traditional way of operating a domestic ESS is based on charging the battery as soon as the PV generation exceeds the consumption, without taking into account overvoltage events during high PV generation hours; the proposed storage concept improves the traditional approach, thanks to the provision of voltage support. A novel method, based on voltage sensitivity analysis, identifies a common power threshold that triggers the ESSs activation in the feeder. A Belgian residential LV feeder is used as a case study. Time-series simulations based on 1-year load and generation profiles verify the method findings and quantify the ESS size in terms of storage power and energy level.

A Modified Differential Evolution Algorithm With Fitness Sharing for Power System Planning

The application of evolutionary computation methods in search and optimization has been growing over the past few decades. As a promising approach in metaheuristic optimization algorithms, differential evolution (DE) has been attracting increasing attention for wide engineering applications including power engineering. Different from conventional evolutionary algorithms using predefined probability distribution function for mutation process, differential evolution exploits the differences of randomly sampled pairs of objective vectors for its mutation process. Consequently the variation between vectors will outfit the objective functions topographical information toward the optimization process, and therefore provides efficient global optimization capability. However, although DE is shown to be precise, fast as well as robust, its search efficiency will be impaired during solution process with fast descending diversity of population. In this paper, detailed numerical studies are carried out to propose the characterization of the performance of several DE mutation methods with and without fitness sharing scheme. All the approaches using the proposed modified DE are presented on an example in power system planning.

TCSC Allocation Based on Line Flow Based Equations Via Mixed-Integer Programming

Research effort has been given to locate the optimal locations of thyristor-controlled series capacitor (TCSC) and their initial compensation levels using mixed-integer programming (MIP). As a useful technique for combinatorial optimisation over integer and continuous variables, the MIP approach can provide robust performance as well as high computational efficiency while solving complex optimal problems. Previous work using MIP employed dc load flow model ignoring reactive power balance, power loss and transformer tap ratios. In this paper, a new planning method is developed based on recently reported line flow equations and basic linearisation of binary-continuous products. The objectives of the planning strategy are to improve system loadability, voltage profile in the network, as well as to minimise the investment cost by choosing proper locations and settings of devices. Simulation results are presented and discussed for IEEE 9-, 57-, 118-, and 300-bus systems.

Demand profile study of battery electric vehicle under different charging options

An increased research on electric vehicles (EV) and plug-in hybrid electric vehicles (PHEV) deals with their flexible use in electric power grids. Several research projects on smart grids and electric mobility are now looking into realistic models representing the behavior of an EV during charging, including nonlinearities. In this work, modeling, simulation and testing of the demand profile of a battery-EV are conducted. Realistic work conditions for a lithium-ion EV battery and battery charger are considered as the base for the modeling. Simulation results show that EV charging generates different demand profiles into the grid, depending on the applied charging option. Moreover, a linear region for the control of EV chargers is identified in the range of 20-90% state-of-charge (SOC). Experiments validate the proposed model.

EV Charging Facilities and Their Application in LV Feeders With Photovoltaics

Low-voltage (LV) grid feeders with high penetration of photovoltaics (PVs) are often affected by voltage magnitude problems. To solve such issues, previous research has shown that reactive power methods, active power curtailment and grid reinforcement can be used for voltage support, yet showing several limits. We introduce the use of electric vehicle (EV) public charging stations with energy storage system (ESS) as a solution for voltage regulation in LV feeders with PV. A novel method is proposed to determine the ESS charging load required for voltage regulation and compare the results for the different locations in the feeder. With time-series simulations, we quantify the energy size required for a station ESS. A Belgian LV residential grid, modeled using real PV generation and load profiles, is used as case study. The method and simulation results show the effectiveness of using public EV charging facilities with the additional function of voltage regulation in feeders with PV.

A Scenario-Based Approach for Energy Storage Capacity Determination in LV Grids With High PV Penetration

In this paper a new method is proposed to determine the minimum energy storage required to be installed at different locations of a low voltage (LV) grid in order to prevent the overvoltage due to high residential photovoltaic (PV) penetration. The method is based on the voltage sensitivity analysis of feasible scenarios associated with the net injected power into the grid by customers. A new concept is defined based on the remaining power curve (RPC) associated with the local generation and consumption, and the uncertainties related to PV output and load consumption are modeled in some RPCs with different occurrence probabilities without involving the time-series studies problems. The proposed method is capable of modeling output power of PV panels with different orientations as well as different electric vehicle (EV) charging patterns.

Improvement of Local Voltage in Feeders With Photovoltaic Using Electric Vehicles

In low-voltage (LV) feeders with high penetration of photovoltaic (PV), a major issue to be solved is voltage rise due to the active power injection. If no measures are taken, this may lead to generations interruptions and to the malfunctioning of domestic appliances due to non-standard voltage profiles. This paper proposes a storage strategy to alleviate voltage rise in feeders with PV, using coordinated electric vehicle (EV) load as the storage solution. The voltage support strategy is easy to implement practically and it is demonstrated on a test feeder emulating a household with roof-mounted PV and an EV. The results show the effectiveness of using coordinated EV load in feeders with PV to mitigate voltage rise problems.

A co-ordinated dispatch model for electricity and heat in a Microgrid via particle swarm optimization

This paper develops a co-ordinated electricity and heat dispatching model for a Microgrid under a day-ahead environment. In addition to operational constraints, network loss and physical limits are addressed in this model, which were always ignored in previous work. As an important component of the Microgrid, a detailed combined heat and power (CHP) model is developed. The part load performance of CHP is modelled by a curve fitting method. Furthermore, an electric heater is introduced into the model to improve the economy of the Microgrid operation and enhance the flexibility of the Microgrid by electricity–heat conversion. Particle swarm optimization is employed to solve this model for the operation schedule to minimize the total operational cost of the Microgrid by co-ordinating the CHP, electric heater, boiler and heat storage. The efficacy of the model and methodology is verified with different operation scenarios.

Probabilistic Forecasting of Photovoltaic Generation: An Efficient Statistical Approach

A novel efficient probabilistic forecasting approach is proposed to accurately quantify the variability and uncertainty of the power production from photovoltaic (PV) systems. Distinguished from most existing models, a linear programming-based prediction interval construction model for PV power generation is established based on an extreme learning machine and quantile regression, featuring high reliability and computational efficiency. The proposed approach is validated through the numerical studies on PV data from Denmark.

Application of Network-Constrained Transactive Control to Electric Vehicle Charging for Secure Grid Operation

This paper develops a network-constrained transactive control method to integrate distributed energy resources (DERs) into a power distribution system with the purpose of optimizing the operational cost of DERs and power losses of the distribution network, as well as preventing grid problems including power transformer congestion and voltage violations. In this method, a price coordinator is introduced to facilitate the interaction between the distribution system operator and the aggregators in the smart grid. Electric vehicles are used to illustrate the proposed network-constrained transactive control method. Mathematical models are presented to describe the operation of the control method. Finally, simulations are presented to show the effectiveness of the proposed method. To guarantee its optimality, we also checked the numerical results obtained with the network-constrained transactive control method and compared them with the one solved by centralized control, and found a good performance of the proposed control method.