Application of stochastic and evolutionary methods to plan for the installation of energy storage in voltage constrained LV networks

Energy storage is widely considered to be an important component of a decarbonised power system if large amounts of renewable generation are to provide reliable electricity. However, storage is a highly capital intensive asset and clear business cases are needed before storage can be widely deployed. A proposed business case is using storage to prevent overvoltage in low voltage (LV) distribution networks to enable residential photovoltaic systems. Despite storage being widely considered for use in LV networks, there is little work comparing where storage might be installed in LV networks from the perspective of the owners of distribution networks (DNOs). This work addresses this in two ways. Firstly, a tool is developed to examine whether DNOs should support a free market for energy storage in which customers with PV purchase storage (e.g. battery systems) to improve their self-consumption. This reflects a recent policy in Germany. Secondly, a new (published) method is developed which considers how DNOs should purchase and locate storage to prevent overvoltage. Both tools use a snapshot approach by modelling the highest and lowest LV voltages. On their own, these tools enable a DNO to determine the cost of energy storage for a particular LV network with a particular set of loads and with PV installed by a given set of customers. However, in order to predict and understand the future viability of energy storage it is valuable to apply the tools to a large number of LV networks under realistic future scenarios for growth of photovoltaics in the UK power system. Therefore, the work extracts over 9,000 LV network models containing over 40,000 LV feeders from a GIS map of cables provided by one of the UK’s electricity distribution networks- Electricity North West. Applying the proposed tools to these 9,000 network models, the work is able to provide projections for how much LV energy storage would be installed under different scenarios. The cost of doing so is compared to the existing method of preventing reinforcement- LV network reconductoring. This is a novel way of assessing the viability of LV energy storage against traditional approaches and allows the work to draw the following conclusions about the market for energy storage in LV distribution networks in the UK: - Overvoltage as a result of PV could begin to occur in the next few years unless UK regulations for voltage levels are relaxed. There could be a large cost (hundreds of millions of pounds) to prevent this if the traditional approach of reconductoring is used. - If overvoltage begins to occur, a free market for energy storage (randomly purchased by electricity consumers) cannot offer large benefits to DNOs in reducing the reinforcement cost unless this is properly controlled, located and/or widely installed by customers. - Optimally located storage by the DNO can reduce overall reinforcement costs to mitigate overvoltage. This would enable more energy storage to balance renewable generation and present large savings to the power system. The exact topology of storage and the storage rating in each LV network could be determined using the tool proposed in this work

Maximising Penetration of Distributed Generation in Existing Urban Distribution Network (UDN)

Electrical power generation is currently moving towards greater penetration of distribution generation (DG), using multiple small generators instead of fewer and larger units. This can potentially create improvements in efficiency, by allowing use of waste heat (cogeneration). However, it also generates new problems related to control and co-ordination of large numbers of DGs, usually connected across the urban distributed network (UDN). In particular, concerns about security of supply and reliability together with the integration of new energy resources, are presenting a number of new challenges to system operators. One of the major changes that are being observed is the connection of significant levels of generation to the UDN. To accommodate this new type of generation the existing UDN should be utilised and developed in an optimal manner. It is well known that present arrangements for planning, dispatching and protection of central power generators are not directly applicable to the new technology. This thesis presents a mathematical method that facilitates the large scale integration of CHP generation, as the most common type of DG, connected onto the UDN. A new methodology is developed to determine the optimal allocation and, size of CHP generation capacity with respect to the technical, environmental and economic constraints of the UDN. The method estimates the adverse impact of any particular constraints with respect to the size and location of DG/CHP plants connected into the UDN. Also, the method provides the basis for quantifying the contribution that DG/CHP units makes to the security of energy supply i.e to what extent the particular DG/CHP can reduce the operational performance demand for the UDN facilities and substitute for the network assets. The method is implemented and tested on a 34 busbars network that represents a section of an UDN. The impact of CHP generation on losses in the UDN is also analysed and incorporated into the optimal capacity allocation methodology. The installation of CHP generation is leading to a major change in the way UDNs are designed and operated. UDNs are now used as a media to connect geographically distributed energy generation to the electrical power system, thereby converting what were originally energy supply networks to be used both for distribution and harvesting of energy. A mathematical model in the form of a Multiple Regression Analysis is presented in order to determine the maximum capacity of CHP generation that may be connected in a given area, while taking account of connection costs as well as technical, environmental, economic and operational setting constraints. Results obtained from various analyses related to the network performance and management are used as data for multiple regression analysis. These analyses include: load flow, fault analysis, environmental and economic analysis. The increased applications of CHP generation presents a substantial challenge to the existing connection policies used to connect CHP plant into UDNs. The section of a typical Irish UDN is used as a case study, and with reference to the available network parameters, the cost and benefits of CHP generations are determined under a number of planning and operational strategies. It is shown that a substantial increase in the net benefits of CHP generation is gained if the appropriate connection method is applied from the start and equally that significant CHP generation connection costs are sustained if ad hoc methods are employed. Connection of CHP generation can profoundly alter the operation of a UDN. Where CHP generation capacity is comparable to or larger than local demand there are likely to be observable impacts on network power flows and voltage regulation. In fact, two major problems to be considered are the voltage levels and operation of protection during faults and disturbances. New connection of CHP generation must be evaluated to identify and quantify any adverse impact on the security and quality of local electricity supplies. There are a number of well-established methods to deal with adverse impacts caused by CHP generation connection into a UDN. While a range of options exist to mitigate adverse impacts, under current commercial arrangements the developer will largely bear the financial responsibility for their implementation. The economic implication can make potential schemes less attractive and in some instances have been an impediment to the development of CHP generation in urban areas. Development of a CHP generation system connection algorithm corresponding to the Least Cost Technically Acceptable (LCTA) method is absolutely vital in order to maximise the penetration of CHP generation into existing UDN with respect to different UDN/CHP system operational settings/constraints and minimal economic implication. In this thesis, results from a number of mitigation methods analysis are compared and used to create the connection process algorithm. This algorithm equally can be applied in the connection process of other distribution generation technologies into existing UDNs

Optimisation of residential battery integrated photovoltaics system: analyses and new machine learning methods

Modelling and optimisation of battery integrated photovoltaics (PV) systems require a certain amount of high-quality input PV and load data. Despite the recent rollouts of smart meters, the amount of accessible proprietary load and PV data is still limited. This thesis addresses this data shortage issue by performing data analyses and proposing novel data extrapolation, interpolation, and synthesis models. First, a sensitivity analysis is conducted to investigate the impacts of applying PV and load data with various temporal resolutions in PV-battery optimisation models. The explored data granularities range from 5-second to hourly, and the analysis indicates 5-minute to be the most suitable for the proprietary data, achieving a good balance between accuracy and computational cost. A data extrapolation model is then proposed using net meter data clustering, which can extrapolate a month of 5-minute net/gross meter data to a year of data. This thesis also develops two generative adversarial networks (GANs) based models: a deep convolutional generative adversarial network (DCGAN) model which can generate PV and load power from random noises; a super resolution generative adversarial network (SRGAN) model which synthetically interpolates 5-minute load and PV power data from 30-minute/hourly data. All the developed approaches have been validated using a large amount of real-time residential PV and load data and a battery size optimisation model as the end-use application of the extrapolated, interpolated, and synthetic datasets. The results indicate that these models lead to optimisation results with a satisfactory level of accuracy, and at the same time, outperform other comparative approaches. These newly proposed approaches can potentially assist researchers, end-users, installers and utilities with their battery sizing and scheduling optimisation analyses, with no/minimal requirements on the granularity and amount of the available input data

Grid-Connected Distributed Wind-Photovoltaic Energy Management: A Review

Energy management comprises of the planning, operation and control of both energy production and its demand. The wind energy availability is site-specific, time-dependent and nondispatchable. As the use of electricity is growing and conventional sources are depleting, the major renewable sources, like wind and photovoltaic (PV), have increased their share in the generation mix. The best possible resource utilization, having a track of load and renewable resource forecast, assures significant reduction of the net cost of the operation. Modular hybrid energy systems with some storage as back up near load center change the scenario of unidirectional power flow to bidirectional with the distributed generation. The performance of such systems can be enhanced by the accomplishment of advanced control schemes in a centralized system controller or distributed control. In grid-connected mode, these can support the grid to tackle power quality issues, which optimize the use of the renewable resource. The chapter aims to bring recent trends with changing requirements due to distributed generation (DG), summarizing the research works done in the last 10 years with some vision of future trends

Advanced Signal Processing Techniques Applied to Power Systems Control and Analysis

The work published in this book is related to the application of advanced signal processing in smart grids, including power quality, data management, stability and economic management in presence of renewable energy sources, energy storage systems, and electric vehicles. The distinct architecture of smart grids has prompted investigations into the use of advanced algorithms combined with signal processing methods to provide optimal results. The presented applications are focused on data management with cloud computing, power quality assessment, photovoltaic power plant control, and electrical vehicle charge stations, all supported by modern AI-based optimization methods

Contingency Management in Power Systems and Demand Response Market for Ancillary Services in Smart Grids with High Renewable Energy Penetration.

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2017

Modeling, Analyses and Assessment of Microgrids Considering High Renewable Energy Penetration

Microgrids are receiving attention due to the increasing need to integrate distributed generations and to ensure power quality and provide energy surety to the customers. 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 25kV system parameters are scaled down to 12kV and renewable sources including solar PV and wind turbines, an energy storage system, and a natural gas generator 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 droop control and autonomous control for microgrid normal operation in islanded mode and grid-tied mode have been proposed and studied. A novel comprehensive supervisory control scheme has been defined to manage the microgrid transition from or to the bulk grid, and to minimize the transients on voltage and frequency. Detailed analyses for islanding, reconnection, and black start are presented for various conditions. The proposed control techniques accept inputs from local measurements and supervisory controls in order to manage the system voltage and frequency. The monitoring of the microgrid for measuring power quality and control requirements for DGs and storage are modeled. The power quality issues are discussed and indexes are calculated. A novel probabilistic assessment of microgrid reliability has been proposed. At last, several extended researches are presented. An experimental system has been built which includes three 250kW inverters emulating natural gas generator, energy storage, and renewable source. The simulation and experimental results are provided which verifies the analytical presentation of the hardware and control algorithms

Distributed Power Generation Scheduling, Modelling and Expansion Planning

Distributed generation is becoming more important in electrical power systems due to the decentralization of energy production. Within this new paradigm, new approaches for the operation and planning of distributed power generation are yet to be explored. This book deals with distributed energy resources, such as renewable-based distributed generators and energy storage units, among others, considering their operation, scheduling, and planning. Moreover, other interesting aspects such as demand response, electric vehicles, aggregators, and microgrid are also analyzed. All these aspects constitute a new paradigm that is explored in this Special Issue

Optimal distributed generation planning based on NSGA-II and MATPOWER

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe UK and the world are moving away from central energy resource to distributed generation (DG) in order to lower carbon emissions. Renewable energy resources comprise a big percentage of DGs and their optimal integration to the grid is the main attempt of planning/developing projects with in electricity network. Feasibility and thorough conceptual design studies are required in the planning/development process as most of the electricity networks are designed in a few decades ago, not considering the challenges imposed by DGs. As an example, the issue of voltage rise during steady state condition becomes problematic when large amount of dispersed generation is connected to a distribution network. The efficient transfer of power out or toward the network is not currently an efficient solution due to phase angle difference of each network supplied by DGs. Therefore optimisation algorithms have been developed over the last decade in order to do the planning purpose optimally to alleviate the unwanted effects of DGs. Robustness of proposed algorithms in the literature has been only partially addressed due to challenges of power system problems such multi-objective nature of them. In this work, the contribution provides a novel platform for optimum integration of distributed generations in power grid in terms of their site and size. The work provides a modified non-sorting genetic algorithm (NSGA) based on MATPOWER (for power flow calculation) in order to find a fast and reliable solution to optimum planning. The proposed multi-objective planning tool, presents a fast convergence method for the case studies, incorporating the economic and technical aspects of DG planning from the planner‟s perspective. The proposed method is novel in terms of power flow constraints handling and can be applied to other energy planning problems