Distributed photovoltaic systems: Utility interface issues and their present status. Intermediate/three-phase systems

The interface issues between the intermediate-size Power Conditioning Subsystem (PCS) and the utility are considered. A literature review yielded facts about the status of identified issues

Distributed photovoltaic systems: Utility interface issues and their present status

Major technical issues involving the integration of distributed photovoltaics (PV) into electric utility systems are defined and their impacts are described quantitatively. An extensive literature search, interviews, and analysis yielded information about the work in progress and highlighted problem areas in which additional work and research are needed. The findings from the literature search were used to determine whether satisfactory solutions to the problems exist or whether satisfactory approaches to a solution are underway. It was discovered that very few standards, specifications, or guidelines currently exist that will aid industry in integrating PV into the utility system. Specific areas of concern identified are: (1) protection, (2) stability, (3) system unbalance, (4) voltage regulation and reactive power requirements, (5) harmonics, (6) utility operations, (7) safety, (8) metering, and (9) distribution system planning and design

HVAC-based hierarchical energy management system for microgrids

With the high penetration of renewable energy into the grid, power fluctuations and supply-demand power mismatch are becoming more prominent, which pose a great challenge for the power system to eliminate negative effects through demand side management (DSM). The flexible load, such as heating, ventilation, air conditioning (HVAC) system, has a great potential to provide demand response services in the electricity grids. In this thesis, a comprehensive framework based on a forecasting-management optimization approach is proposed to coordinate multiple HVAC systems to deal with uncertainties from renewable energy resources and maximize the energy efficiency. In the forecasting stage, a hybrid model based on Multiple Aggregation Prediction Algorithm with exogenous variables (MAPAx)-Principal Components Analysis (PCA) is proposed to predict changes of local solar radiance, vy using the local observation dataset and real-time meteorological indexes acquired from the weather forecast spot. The forecast result is then compared with the statistical benchmark models and assessed by performance evaluation indexes. In the management stage, a novel distributed algorithm is developed to coordinate power consumption of HVAC systems by varying the compressors’ frequency to maintain the supply-demand balance. It demonstrates that the cost and capacity of energy storage systems can be curtailed, since HVACs can absorb excessive power generation. More importantly, the method addresses a consensus problem under a switching communication topology by using Lyapunov argument, which relaxes the communication requirement. In the optimization stage, a price-comfort optimization model regarding HVAC’s end users is formulated and a proportional-integral-derivative (PID)-based distributed algorithm is thus developed to minimize the customer’s total cost, whilst alleviating the global power imbalance. The end users are motivated to participate in energy trade through DSM scheme. Furthermore, the coordination scheme can be extended to accommodate battery energy storage systems (BESSs) and a hybrid BESS-HVAC system with increasing storage capacity is proved as a promising solution to enhance its selfregulation ability in a microgrid. Extensive case studies have been undertaken with the respective control strategies to investigate effectiveness of the algorithms under various scenarios. The techniques developed in this thesis has helped the partnership company of this project to develop their smart immersion heaters for the customers with minimum energy cost and maximum photovoltaic efficiency

Smart Microgrids: Optimizing Local Resources toward Increased Efficiency and a More Sustainable Growth

Smart microgrids are a possibility to reduce complexity by performing local optimization of power production, consumption and storage. We do not envision smart microgrids to be island solutions but rather to be integrated into a larger network of microgrids that form the future energy grid. Operating and controlling a smart microgrid involves optimization for using locally generated energy and to provide feedback to the user when and how to use devices. This chapter shows how these issues can be addressed starting with measuring and modeling energy consumption patterns by collecting an energy consumption dataset at device level. The open dataset allows to extract typical usage patterns and subsequently to model test scenarios for energy management algorithms. Section 3 discusses means for analyzing measured data and for providing detailed feedback about energy consumption to increase customers’ energy awareness. Section 4 shows how renewable energy sources can be integrated in a smart microgrid and how energy production can be accurately predicted. Section 5 introduces a self-organizing local energy system that autonomously coordinates production and consumption via an agent-based energy auction system. The final section discusses how the proposed methods contribute to sustainable growth and gives an outlook to future research

Distributed electric-spring-based smart thermal loads for overvoltage prevention in LV distributed network using dynamic consensus approach

Overvoltage arising from reverse power flow in low-voltage (LV) distribution network caused by surplus roof-top photovoltaic (PV) energy generation is a major challenge in the emerging smart grid. This paper reports a study on the use of distributed thermal Smart Loads (SLs) for overvoltage prevention along a LV feeder. The basic principle involves the combined use of electric springs (ESs) and storage-type electric water heaters (EWHs) as distributed smart loads. Through distributed control, these smart loads play the important roles of mitigating reverse power flow problems and maintaining local mains voltage within the specified tolerance. Detailed modeling of the combined ES and EWH including their practical electrical and thermal capacities and constraints is adopted and optional distributed energy storage system (ESS) is also considered in the evaluation. Based on the Sha Lo Bay residential LV network in Lantau Island, Hong Kong, these case studies confirm the feasibility of the proposed approach for overvoltage prevention. The proposed distributed SLs-plus-ESS method is proved to be a cost-effective and environmental friendly way for overvoltage prevention in LV distributed network with high PV penetration

Bidding strategy for a virtual power plant for trading energy in the wholesale electricity market

Virtual power plants (VPPs) are an effective way to increase renewable integration. In this PhD research, the concept design and the detailed costs and benefits of implementing a realistic VPP in Western Australia (WA), comprising 67 dwellings, are developed. The VPP is designed to integrate and coordinate an 810kW rooftop solar PV farm, 350kW/700kWh vanadium redox flow batteries (VRFB), heat pump hot water systems (HWSs), and smart appliances through demand management mechanisms. This research develops a robust bidding strategy for the VPP to participate in both load following ancillary service (LFAS) and energy market in the wholesale electricity market in WA considering the uncertainties associated with PV generation and electricity market prices. Using this strategy, the payback period can be improved by 3 years (to a payback period of 6 years) and the internal rate of return (IRR) by 7.5% (to an IRR of 18%) by participating in both markets. The daily average error of the proposed robust method is 2.7% over one year when compared with a robust mathematical method. The computational effort is 0.66 sec for 365 runs for the proposed method compared to 947.10 sec for the robust mathematical method. To engage customers in the demand management schemes by the VPP owner, the gamified approach is adopted to make the exercise enjoyable while not compromising their comfort levels. Seven gamified applications are examined using a developed methodology based on Kim’s model and Fogg’s model, and the most suitable one is determined. The simulation results show that gamification can improve the payback period by 1 to 2 months for the VPP owner. Furthermore, an efficient and fog-based monitoring and control platform is proposed for the VPP to be flexible, scalable, secure, and cost-effective to realise the full capabilities and profitability of the VPP

Models and Optimal Controls for Smart Homes and their Integration into the Electric Power Grid

Smart homes can operate as a distributed energy resource (DER), when equipped with controllable high-efficiency appliances, solar photovoltaic (PV) generators, electric vehicles (EV) and energy storage systems (ESS). The high penetration of such buildings changes the typical electric power load profile, which without appropriate controls, may become a “duck curve” when the surplus PV generation is high, or a “dragon curve” when the EV charging load is high. A smart home may contribute to an optimal solution of such problems through the energy storage capacity, provided by its by battery energy storage system (BESS), heating, ventilation, and air conditioning (HVAC) system, and electric water heater (EWH), and the advanced controls of an home energy management (HEM). The integrated modeling of home energy usage and electric power distribution system, developed as part of this dissertation research, provides a testbed for HEM control methods and prediction of long-term scenarios. A hybrid energy storage system including batteries and a variable power EWH was proposed. It was demonstrated that when the operation of the proposed hybrid energy storage system was coordinated with PV generation, the required battery capacity would be substantially reduced while still maintaining the same functionality for smart homes to operate as dispatchable generators. A newly developed co-simulation framework, INSPIRE+D, enables the dynamic simulation of smart homes and their connection to the grid. The equivalent thermal model of a reference house was proposed with parameters based on the systematic study of experimental data from fully instrumented field demonstrators. Energy storage capacity of HVAC systems was calculated and an equivalent state-of-charge (SOC) was defined. The aggregated HVAC load was calculated based on special HVAC parameters and a sequential DR scheme was proposed to reduce both ramping rate and peak power, while maintaining human comfort according to ASHRAE standards. A long short-term memory (LSTM) method was applied to for the identification of HVAC system from the aggregated data. The generic water heater load curves based on the data retrieved from large experimental projects for resistive EWHs and heat pump water heaters (HPWHs) were created. A community-level digital twin with scalability has been developed to capture the aggregated hot water flow and average hot temperature in the tanks. The potential electricity saving of shifting from EWH to HPWH was calculated. The energy storage capacities for both EWHs and HPWHs were calculated. Long term load prediction by considering different fractions of smart homes with HEM for at the power system was provided based on one of the largest rural field smart energy technology demonstrators located in Glasgow, KY, US. Also demonstrates was the ability of EWH to provide ancillary services while maintaining customer comfort. The minimum participation rates for EWH and batteries were calculated and compared with respect to different peak reduction targets. The aggregated charging load for EV in a community was calculated based on data from the National Travel Household Survey (NHTS). The EV charging and RESS operation were scheduled to reduce the daily utility charge. Building resilience was quantified by analyzing the self-sustainment duration for all possible power outages throughout an entire year based on the annual electricity usage of a typical California residence. The influence of factors such as energy use behavioral patterns, BESS capacity, and an availability of EV was evaluated. A concept of generalized energy storage (GES) model for BESS, EWH and HVAC systems was proposed. The analogies, including SOC versus water/indoor temperature differential, were identified and explained, and models-in-the-loop (MIL) were introduced, which were compatible with the Energy Star and Consumer Technology Association (CTA)-2045 general specifications and command types. A case study is included to illustrate that the “energy content” and “energy take” for BESS and EWH. The main original contributions of this dissertation include the comprehensive simulation of the total building energy usage and the development of the co-simulation framework incorporating building and power system simulators. Another contribution of the dissertation is the quantification of building resilience based on the building energy usage model. The dissertation also contributes to the concept of GES which regards the HVAC and EWH as virtual energy storage and their unified controls with BESS. The GES facilitates the employment of industrial standards, e.g., CTA-2045, and the hybrid ESS reduces required BESS capacity. This dissertation contributes to the modeling of aggregated load for EWH, HVAC, and EV using different methods and long term forecasting of power profile at the system level. The aggregated generic load for EWH was calculated based on large amount of field data, the aggregated EV charging load was estimated based on national survey results, and the aggregated HVAC load was simulated based on the modeling of every residences, where the model parameters were populated according to special distributions. The methods based LSTM for the identification of HVAC power from the aggregated load was developed

Islanding Detection and Power Quality Analysis in Microgrid

芝浦工業大学2019年

Microgrids:experiences, barriers and success factors

Although microgrids have been researched for over a decade and recognized for their multitude of benefits to improve power reliability, security, sustainability, and decrease power costs for the consumer, they have still not reached rapid commercial growth. The main aim of this research is to identify the common barriers and ultimate success factors to implementing a microgrid in the real world. We found that microgrids vary significantly depending on location, components, and optimization goals, which cause them to experience different types of challenges and barriers. However, the most common barriers were identified and grouped into four categories: technical, regulatory, financial, and stakeholder, based on the literature and overlying patterns recognized amongst the thirteen case studies. The most common technical barriers include problems with technology components, dual-mode switching from grid-connected to island mode, power quality and control, and protection issues. There is extensive research on how to overcome these issues, so technical solutions are becoming available yet case specific. Regulatory barriers exist due to interconnection rules with the main grid and the prohibition of bi-directional power flow and local power trading between microgrid and the main network. The latter issue is the barrier experienced most often and has only recently been addressed, so solutions need further research. The main financial barrier is still the burden of high investment and replacement costs of the microgrid. This can be resolved with proper market support in the short term and might naturally resolve itself through learning over the long run. Lastly, stakeholder barriers include issues with conflicting self-interest and trust, and having the expertise to manage operations. These stakeholder barriers are not yet addressed in the literature and need to be further researched

Control and Energy Management of Standalone Interconnected AC Microgrids

This thesis considered microgrids as local area distribution mini-power grids formed by distributed generation sources, energy storage systems and loads. They are reliable and can operate at different voltages and frequencies to meet the requirements of the load. Microgrids have limited renewable energy source (RES) capacity, which can only supply a limited load and increasing the load beyond a specifically designed limit can lead to stability issues. Irrespective of its limited capacity, there has been an increased widespread deployment of renewable energy-based microgrids worldwide orchestrated by the 2015 Paris Agreement and the war in Ukraine and as a solution to meet the global demand for energy in electricity deficit zones aimed to achieve universal access to affordable, reliable, and sustainable energy. Fast forward to the future, flooded singly operated microgrids face the problem of more curtailing of RES and load shedding. Multiple microgrids can be interconnected to mitigate the limitations of single microgrids and improve supply reliability, enhance power supply availability, stability, reserve capacity, reduce investment in new generating capacity and control flexibility. As a result, this thesis proposes a new structure and control technique for interconnecting multiple standalone AC microgrids to a common alternating current (AC) bus using a back-to-back power electronic converter and a traditional transformer. Each microgrid considered in this thesis comprises a renewable energy source (RES), battery, auxiliary unit, and load. The battery maintains the AC bus voltage and frequency and balances the difference in power generated by the RES and that consumed by the load. Each microgrid battery’s charge/discharge is maintained within the safest operating limit to maximise the RES power utilisation. The back-to-back converters are used to decouple the connecting standalone microgrid frequencies and facilitate power exchange between microgrids. The transformer is used to transmit electric power over long distances efficiently. The control technique for all the connecting bidirectional back-to-back converters is developed to manage the bidirectional power flow between each microgrid and other microgrids in the network and to balance the energy in the global bus of the interconnected microgrid with no communication. The control strategy uses a frequency signalling mechanism to limit the power demand of individual global converters and adjusts its droop coefficients accordingly and in proportion to deviation in frequency. The global droop controllers of the global connecting converters receive information about the status of the frequencies of individual microgrids using a low bandwidth communication link to enhance network power flow. MATLAB/Simulink results validate the performance of the proposed structure and control strategy. A decentralised control scheme is further proposed for the standalone interconnected AC microgrid structure. This thesis presented a high-level global droop controller that exchanges power between the interconnected microgrids. Renewable power curtailment and auxiliary power supplement mechanisms are designed based on the bus frequency signalling technique to achieve balance and continuity of supply. In case of power shortage in one microgrid, priority will first be given to power import from other microgrids. A power supplement is used if the power imported is insufficient to control the battery state of charge (SOC). Similarly, in case of a power surplus, priority will be given to power export, and if this is not enough, power from RES will be curtailed. Performance evaluation shows that the proposed controller maximises renewable power utilisation and minimises auxiliary power usage while providing better load support. The performance validation of the proposed structure and control strategy has been tested using MATLAB/Simulink. Furthermore, this thesis investigated a centralised control and energy management of multiple interconnected standalone AC microgrids using the Nelder-Mead simplex algorithm (Fminsearch optimisation toolbox in MATLAB) based on the new proposed model. The main objective is to minimise the total cost of energy from the auxiliary unit produced from gas. The results obtained are compared with those obtained from an unoptimised system. The performance evaluation investigation results are compared with the unoptimised results to determine the percentage optimal performance of the system. The comparison outcome shows that the proposed optimisation method minimises the total auxiliary energy cost by about 9% compared with the results of the unoptimised benchmark