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

Roadmaps for heating and cooling system transitions seen through uncertainty and sensitivity analysis

Future district heating systems should enable efficient and economic energy supply, which can be achieved by lowering the system temperatures and boosting it at demand-side. Current solutions include the ultra-low-temperature district heating (ULTDH) and fifth generation district heating and cooling (5GDHC) systems. The transition towards these systems is subject to multiple future uncertainties such as the energy price, investment cost, and demand changes, which were missing in previous works. To investigate the effects of these uncertainties on conclusions brought by established design roadmaps for future DHCs, a five-step framework, which combines the energy system optimization with stochastic simulations, uncertainty analysis and sensitivity assessment, is developed in this study. The framework is applied on a hypothetical 0.25 km2 square district with varying uncertain parameters. Based on stochastic cases, the index named cost-saving probability (CSP) is utilized to reflect the potential of being economic attractive when comparing the energy systems. For the transition towards the ULTDHC, 5GDHC, and individual systems, the most sensitive factors for the CSP are the area demand density, overlapping heating and cooling demand, and linear demand density, respectively. The investment in thermal energy storage (TES) becomes important only when the integration of a larger share of renewable energy is targeted. A roadmap summarizing the promoting and hindering factors for the system transition is provided, pointing out the future focus area for DHC design. The results from the sensitivity analysis also revealed the limited role of TES in integrating variable renewable energy in high-efficiency DHC systems

Assessment and mitigation of voltage violations by solar panels in a residential distribution grid

Distributed renewable electricity generators, such as solar cells and wind turbines introduce bidirectional energy flows in the low-voltage power grid, possibly causing voltage violations and grid instabilities. The current solution to this problem comprises automatically switching off some of the local generators, resulting in a loss of green energy. In this paper we study the impact of different solar panel penetration levels in an residential area and the corresponding effects on the distribution feeder line. To mitigate these problems, we assess how effective it is to locally store excess energy in batteries. A case study on a residential feeder serving 63 houses shows that if 80% of them have photo-voltaic (PV) panels, 45% of them would be switched off, resulting in 482 kWh of PV-generated energy being lost. We show that providing a 9 kWh battery at each house can mitigate some voltage violations, and therefor allowing for more renewable energy to be used

Comparison of centralised and distributed battery energy storage systems in LV distribution networks on operational optimisation and financial benefits

The integration of renewable energy sources and plug-in electric vehicles (PEVs) into the existing low-voltage (LV) distribution network at a high penetration level can cause reverse power flow, increased overall energy demand, network congestion, voltage rise/dip, transformer overloading and other operational issues. In this study, these potentially negative impacts caused by increasing penetration of distributed energy resources and PEVs are stochastically quantified based on a real practical 400 V distribution network as a case study. Battery energy storage (BES) is known to be a promising method for peak shaving and to provide network ancillary services. Two types of BES implementations aiming at distinctive charging and discharging targets without communication infrastructure or control centre are proposed and simulated. Optimisation results and potential financial profit of these two BES systems are compared and discussed in detail

Coordinated Volt-Var control in multiple smart inverters in Smart Distribution Networks for Voltage Regulation.

The inevitable growing demand for electrical power, depleting sources of conventional power generation, and world wide concern about global warming are major factors to boost the trend of renewable integration in grids. This rising trend is causing many technical and operational challenges where one of the most prominent problem is the overvoltage caused by distributed generation units, interfaced at the consumer end, and power injections at random nodes. This in contrast with predefined power flows of conventional grids gives rise to bidirectional power flows that demand for modern, coordinated and robust voltage regulation scheme with minimal communication infrastructure. A centralized, coordinated, differential evolution based Volt/VAR regulation scheme is proposed to eliminate the voltage deviations caused by excessive photovoltaic integration in distribution systems. Time step simulation utilizing OpenDSS interfaced with MATLAB on standard IEEE-123 feeder are implemented to test the effectiveness of the proposed scheme

Stochastic analysis and reliability-cost optimization of distributed generators and air source heat pumps

This paper presents a framework for stochastic analysis, simulation and optimisation of electric power grids combined with heat district networks. In this framework, distributed energy sources can be integrated within the grids and their performance is modelled. The effect of uncertain weather-operational conditions on the system cost and reliability is considered. A Monte Carlo Optimal Power Flow simulator is employed and statistical indicators of the system cost and reliability are obtained. Reliability and cost expectations are used to compare 4 different investments on heat pumps and electric power generators to be installed on a real-world grid. Generators' sizes and positions are analysed to reveal the sensitivity of the cost and reliability of the grid and an optimal investment problem is tackled by using a multi-objective genetic algorithm

ECONOMIC AND POLICY ANALYSIS FOR SOLAR PV SYSTEMS IN INDIANA

In recent years, the energy market in the US and globally is expanding the production of renewable energy. With other energy sources, solar energy for electricity is also expanding in the US. Indiana is one of the states expanding solar energy with solar PV systems. However, the economics of solar PV systems in Indiana have not been analyzed and electricity customers in Indiana are not informed enough about the economics of solar PV systems. Therefore, we conduct benefit cost analysis with several uncertain input variables to determine the economics of adopting solar PV systems in Indiana based on policy instruments that could increase adoption of solar PV systems. The specific objectives of this study are analyses of the cost distribution of solar PV systems compared with grid electricity in homes and on the probability that solar can be less than current electricity from grids under different combinations of policies. We first do the analysis under current policy options and then do the analysis under potential policy options for a variety of scenarios. With the information addressed in our study, customers can be informed how beneficial or not it would be to adopt solar PV systems in their homes. Also, government can be informed how effective policies can be and how to manage policy options for encouraging solar PV systems. The results show that the current policies are important in reducing the cost of solar PV systems. However, with current policies, there is only 50-50 chance of solar being cheaper than electricity from grids. However, if potential policies are implemented, solar PV systems can be more economical than electricity from the grids. Thus, it is arguable that government still should implement other policies to encourage people to adopt solar PV systems in Indiana

Modeling Storage and Demand Management in Electricity Distribution Grids

Storage devices and demand control may constitute beneficial tools to optimize electricity generation with a large share of intermittent resources through inter-temporal substitution of load. We quantify the related cost reductions in a simulation model of a simplified stylized medium-voltage grid (10kV) under uncertain demand and wind output. Benders Decomposition Method is applied to create a two-stage stochastic program. The model informs an optimal investment sizing decision as regards specific 'smart grid' applications such as storage facilities and meters enabling load control. Model results indicate that central storage facilities are a more promising option for generation cost reductions as compared to demand management. Grid extensions are not appropriate in any of our scenarios. A sensitivity analysis is applied with respect to the market penetration of uncoordinated Plug-In Electric Vehicles which are found to strongly encourage investment into load control equipment for `smart` charging and slightly improve the case for central storage devices.Storage, demand management, stochastic optimization, Benders Decomposition