SP - TSG - 2012

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Abstract
As solar photovoltaic power generation becomes more commonplace, the inherent intermittency of the solar resource poses one of the great challenges to those who would design and implement the next generation smart grid. Specifically, grid-tied solar power generation is a distributed resource whose output can change extremely rapidly, resulting in many issues for the distribution system operator with a large quantity of installed photovoltaic devices. Battery energy storage systems are increasingly being used to help integrate solar power into the grid. These systems are capable of absorbing and delivering both real and reactive power with sub-second response times. With these capabilities, battery energy storage systems can mitigate such issues with solar power generation as ramp rate, frequency, and voltage issues. Beyond these applications focusing on system stability, energy storage control systems can also be integrated with energy markets to make the solar resource more economical. Providing a high-level introduction to this application area, this paper presents an overview of the challenges of integrating solar power to the electricity distribution system, a technical overview of battery energy storage systems, and illustrates a variety of modes of operation for battery energy storage systems in grid-tied solar applications. The real-time control modes discussed include ramp rate control, frequency droop response, power factor correction, solar time-shifting, and output leveling.
This paper presents a dynamic modeling and control strategy for a sustainable microgrid primarily powered by wind and solar energy. A current-source-interface multiple-input dc-dc converter is used to integrate the renewable energy sources to the main dc bus. Potential suitable applications range from a communication site or a residential area. A direct-driven permanent magnet synchronous wind generator is used with a variable speed control method whose strategy is to capture the maximum wind energy below the rated wind speed. This study considers both wind energy and solar irradiance changes in combination with load power variations. As a case study a 30-kW wind/solar hybrid power system dynamic model is explored. The examined dynamics shows that the proposed power system is a feasible option for a sustainable microgrid application.
Wind and solar power are well known intermittent power sources with high availability uncertainties. Hence, whenever they are integrated to distribution systems, these power sources can increase significantly the complexity of system operation. This paper presents an impact analysis of distributed energy resources integration on distribution systems, focusing mainly on reliability aspects. Therefore, an interesting algorithm to correctly determine the amount of capacity that may be transferred to other feeders is presented and discussed, taken into consideration the presence of distributed generation. The methodology is tested in a typical Brazilian distribution system, assuming the integration of a diesel-based combined heat and power unit, wind turbines, and solar panels. The results provide general insights regarding the benefits of applying distributed generation to alleviate load transfer restrictions.
"Many countries have provided incentives for homeowners, businesses and other entities to install solar photovoltaics (PV) to offset generation from other sources, to reduce the pollution caused by the combustion of fossil fuels. Where incentives are high, significant amounts of solar PV are being installed. The term, high penetration solar, has been defined a number of different ways; sometimes comparing the amount of generation to the peak load on a feeder or to the lowest load on a feeder. In this paper we will use it to mean two things: a high enough localized penetration to cause potential voltage issues and a high enough aggregate penetration on a feeder or other section of the electric grid to cause potential voltage issues. The purpose of the paper will be to discuss some of the problems occurring at various voltage levels of the system and solutions that can be or are being introduced to address the issues that high penetration solar PV creates."
The integration of photovoltaic (PV) generating stations in the power grids requires the amount of power available from the PV to be estimated for power systems planning on yearly basis and operation control on daily basis. To determine the PV station maximum output power, the PV panels must be placed at an optimal tilt angle to absorb maximum energy from the sun. This optimal tilt angle is a nonlinear function of the location, time of year, ground reflectivity and the clearness index of the atmosphere. This paper proposes a neural network (NN) to estimate the optimal tilt angle at a given location and thus an estimate of the amount of energy available from the PV in a microgrid.
This paper proposes a coordinated control of distributed energy storage system (ESS) with traditional voltage regulators including the on-load tap changer transformers (OLTC) and step voltage regulators (SVR) to solve the voltage rise problem caused by the high photovoltaic (PV) penetration in the low-voltage distribution network. The main objective of this coordinated control is to relieve the tap changer transformer operation stress, shave the distribution network peak load and decrease the transmission and distribution resistive power losses under high solar power penetration. The proposed control method limits the energy storage depth of discharge in order to meet a more than ten-year cycle life. A benchmark distribution network model was developed in the Real Time Digital Simulator (RTDS) and the simulation results from the studied cases verified the proposed coordinated control strategy. The experimental implementation of proposed control algorithms were developed based on a power hardware-in-the-loop (PHIL) test bed with a 22 kWh ESS, a smart meter, Labview controller, and RTDS. The experimental results were consistent with those obtained from simulation study.
Microgrids are receiving attention due to the increasing need to integrate distributed generations and to insure power quality and to provide energy surety to critical loads. Since renewables need to be in the mix for energy surety, a high renewable-energy penetrated microgrid is analyzed in this paper. The standard IEEE 34 bus distribution feeder is adapted and managed as a microgrid by adding distributed generation and load profiles. The 25 kV system parameters are scaled down to 12 kV and renewable sources including solar PV and wind turbines, an energy storage system, and a diesel generator for islanded mode have been added to the 34-bus system. The distribution generations (DG) and renewables are modeled in detail using PSCAD software and practical constraints of the components are considered. The monitoring of the microgrid for measuring power quality and control requirements for these DGs and storage are modeled to maintain the power quality of the system when loads are varied. Renewable sources are modeled with seasonal variation at different locations. The microgrid is monitored at number of buses and the power quality issues are measured and indexes are calculated. This paper proposes a generalized approach to design (determine the capacity requirements) and demonstrates the management of microgrids with metrics to meet the power quality indexes.
The grid faces a number of challenges related to large-scale integration of intermittent distributed generation (DG) such as photovoltaics (PV). Power quality challenges include voltage regulation issues, flicker, and frequency volatility. Operational challenges include the need for extension of the command-and-control infrastructure to millions of devices anticipated on the low-voltage (service) side of the distribution network. This paper presents an advanced grid-tied inverter controls concept designed to address suchchallenges. This controls concept is based on reproducing favorable characteristics of traditional generators that result in load-following tendencies, and is accordingly dubbed Generator Emulation Controls (GEC). Traditional generators are analyzed with specific focus on such favorable characteristics as inertial dynamics and controlled impedance. Details of GEC are then presented, and its implementation is outlined based on the evolution of conventional grid-tied inverter controls. This is followed by an examination of the system impact of GEC-operated devices. GEC allows DG inverters to perform voltage regulation support, reactive power compensation, and fault ride-through. GEC also allows DG inverters to form scalable inverter-based microgrids, capable of operating in grid-tied mode or separating and supporting an islanded load. Simulation results are presented to examine the impact on voltage regulation and power losses across a distribution feeder. Two experimental test beds are used to demonstrate voltage regulation support, transient suppression, and microgridding capabilities.
A large integration of renewable energy sources such as wind power generation and photovoltaic generation causes some problems in power systems, e.g., distribution voltage rise and frequency fluctuation. Battery energy storage system (BESS) is one of the effective solutions to these problems. Due to a high cost of the BESS, an application of controllable loads such as electric vehicle (EV) and heat pump water heater (HPWH) to the power system control is considered in this paper for reduction of the required capacity of the BESS. This paper proposes a new supplementary load frequency control (LFC) method by use of a number of both the EVs and the HPWHs as controllable loads. The effectiveness of the proposed LFC method is shown by numerical simulations conducted on the power system model with a large integration of wind power generation and photovoltaic generation.
Many commercial office buildings have become micro smart grids with on-site power generations, storage devices, and uncertain demands. Due to the pervasive nonlinearity and randomness of such a multi-energy system, simulation is usually the only faithful way to accurately describe the system dynamics and for performance evaluation. However, simulation is usually time-consuming and each sample path provides only noisy observations. Thus finding the optimal energy management policy is nontrivial. In this paper, a joint schedule problem is considered to schedule solar power, wind power, combined cooling, heating, and power generation, high temperature chiller, liquid desiccant fresh air unit, battery, and power grid in order to satisfy the electricity load, sensible heat load, and latent heat load in buildings with the minimal expected cost. We make two major contributions in this paper. First, three simulation-based policy improvement (SBPI) methods are developed to improve from given base policies. Second, the performance of these methods are systematically analyzed through numerical experiments. We show that when there are sufficient computing budget, the SBPI methods improve the given base policies. Different methods are recommended for problems with different computing budget. Sensitivity analysis of the policy and the value of accurate information are also discussed.
Time of use (TOU) pricing is considered by many to be a key part of creating a more energy-efficient and renewable-energy-friendly grid. TOU pricing is also an integral part of the smart grid and is already available to customers of some electric utilities. With TOU pricing becoming a reality, intelligent dispatching systems that utilize energy storage devices (ESDs) to maximize the use of renewable resources, such as energy produced by small, customer owned wind generators and roof-top solar generators, and grid energy while determining the most economic dispatch schedule could play an important role for both the customer and the utility. The purpose of this work is to create an algorithm upon which these dispatching systems can be based. The details of one proposed algorithm are presented. Several case studies are presented to show the effectiveness of the algorithm from both a technical standpoint and an economic standpoint. The case studies show that while the algorithm developed is successful from a technical standpoint, the high cost of energy storage at this time limits its widespread deployment.
A smart building energy system usually contains multiple energy sources such as power grids, autonomous generators, renewable resources, storage devices, and schedulable loads. Storage devices such as batteries, ice/heat storage units, and water tanks play an important role in reducing energy cost in building energy systems since they can help sufficiently utilize renewable energy resources and time-of-use electricity prices. It is important to plan, schedule, and coordinate all the storage devices together with schedulable loads in a building facilitated by microgrid technology. To consider the above problem with uncertainties in solar radiation and demand profiles, a stochastic optimization problem is formulated and solved by the scenario tree method. The best combination and the optimal capacities of storage devices for specific building energy systems are then determined. Furthermore, the optimal operating strategy of building energy systems can be obtained. The performance analysis on the storage devices is conducted and the numerical results show that thermal storage devices (e.g., ice storage units, water tanks) are good for saving energy costs but batteries may not be economical due to their high investment cost and short lifetime. It is also observed that the aforementioned uncertainties have an impact on selecting which type and capacity of storage device should be used.
This paper presents mathematical optimization models of residential energy hubs which can be readily incorporated into automated decision making technologies in smart grids, and can be solved efficiently in a real-time frame to optimally control all major residential energy loads, storage and production components while properly considering the customer preferences and comfort level. Novel mathematical models for major household demand, i.e., fridge, freezer, dishwasher, washer and dryer, stove, water heater, hot tub, and pool pumps are formulated. Also, mathematical models of other components of a residential energy system including lighting, heating, and air-conditioning are developed, and generic models for solar PV panels and energy storage/generation devices are proposed. The developed mathematical models result in Mixed Integer Linear Programming (MILP) optimization problems with the objective functions of minimizing energy consumption, total cost of electricity and gas, emissions, peak load, and/or any combination of these objectives, while considering end-user preferences. Several realistic case studies are carried out to examine the performance of the mathematical model, and experimental tests are carried out to find practical procedures to determine the parameters of the model. The application of the proposed model to a real household in Ontario, Canada is presented for various objective functions. The simulation results show that savings of up to 20% on energy costs and 50% on peak demand can be achieved, while maintaining the household owner's desired comfort levels.
This paper presents a new method based on the cost-benefit analysis for optimal sizing of an energy storage system in a microgrid (MG). The unit commitment problem with spinning reserve for MG is considered in this method. Time series and feed-forward neural network techniques are used for forecasting the wind speed and solar radiations respectively and the forecasting errors are also considered in this paper. Two mathematical models have been built for both the islanded and grid-connected modes of MGs. The main problem is formulatedas a mixed linear integer problem (MLIP), which is solved in AMPL (A Modeling Language for Mathematical Programming). The effectiveness of the approach is validated by case studies where the optimal system energy storage ratings for the islanded and grid-connected MGs are determined. Quantitative results show that the optimal size of BESS exists and differs for both the grid-connected and islanded MGs in this paper.
Despite the significant research efforts devoted to the microgrid and smart grid areas, numerous problems related to real world implementations still remain unsolved. The present special issue was announced with the objective of addressing and disseminating state-of-the-art R&D results on microgrids to bring together researchers from both academia and industry with the goal of fostering interactions among stakeholders. In response, 190 two-page extended abstracts were received and considered for the first round of reviews. Authors of about 60 selected abstracts were then invited to submit the full papers in the second round and out of them 27 high-quality manuscripts were ultimately approved and included in this special issue. These papers are organized according to the following four topics: 1) Microgrid dynamic performance, control/operational strategies, and voltage/frequency regulation. Here, 14 papers deal with the dynamic modeling and performance ofmicrogrid or its building blocks and propose appropriate control/operational strategies or voltage/frequency regulation approaches. 2) Reliability and power quality aspects of microgrids, where seven papers about the microgrid reliability assessment techniques, impact of distributed energy resources on microgrid reliability indices, and the microgrid power quality analysis are presented. 3) Fault modeling and protection schemes. This section consists of four papers focusing on series fault modeling in dc microgrids, overvoltage protection of photovoltaic generators, and protection schemes tailored for the microgrid operational characteristics. 4) Others: Two practical papers about the impact of microgrids on the planning of the medium-voltage distribution networks and characterization of the Vanadium Redox battery are presented.