A Hierarchical Model Predictive Control Approach For Battery Systems

Applications in energy systems often require to simultaneously miti- gate long-term peak and short-term electricity costs. The long-term peak electricity demand cost, known as demand charge, constitutes an important component of the electricity bills for large consumption units like building campuses or manufacturing plants. This poses a challenging multiscale planning problem that should make decisions at fine timescales while mitigating long-term costs. We present a hierarchical model predictive control (MPC) approach to tackle this problem in the context of stationary battery systems. The goal is to determine the optimal charge-discharge policy for the battery to minimize the monthly demand charge. We also perform comparative studies of the proposed hierarchical MPC scheme and standard MPC schemes that use ad-hoc approaches to handle the multiple timescales. In the proposed hierarchical MPC approach, we assume that the state of charge (SOC) policy is periodic, which allows us to cast the long-term planning problem as a tractable stochastic programming problem. Here, very period (e.g., a day or week) represents an operational scenario and we seek to determine targets for the periodic SOC levels and the peak cost. The long-term planner MPC communicates the periodic SOC targets and peak cost to a short-term MPC controller. The short-term MPC determines the intra-period charge/discharge policies (at high resolution) while meeting the targets of the long-term planning. We use a case study for a university campus to demonstrate that the hierarchical MPC scheme yields optimal demand charge and charge-discharge policy under nominal (perfect forecast) conditions. Under imperfect forecasts, we show that the hierarchical MPC scheme results in significant improvements in demand charge reduction over a standard MPC scheme that uses a discounting factor to capture long-term effects

Optimal Sizing of Battery Storage Systems for Industrial Applications when Uncertainties Exist

in fact, we think that the sizing procedure must properly take into account the unavoidable uncertainties introduced by the cost of electricity and the load demands of industrial facilities. Three approaches provided by Decision Theory were applied, and they were based on: (1) the minimization of expected cos

Simultaneous co-integration of multiple electrical storage applications in a consumer setting

In a consumer setting, storage systems can be dispatched in order to shift surplus generation to periods when a local generation deficit exists. However, the high investment cost still makes the deployment of storage unattractive. As a way to overcome this problem existing literature looking at storage installed at the grid-level suggests dispatching the storage device for multiple applications simultaneously in order to access several value streams. Therefore, in this work, a Mixed Integer Linear Program is developed in order to schedule the operation of a storage device in a consumer context for multiple objectives in parallel. Besides shifting locally generated energy in time, the peak demand seen by the electric grid is reduced and the storage device is operated to provide primary reserve control. The model is applied in a case study based on the current German situation in order to illustrate the value contribution of stacking multiple services. When pursuing multiple applications simultaneously, the revenues of storage can be increased significantly. However, the revenues are not additive due to conflicting operations which originates a revenue gap as illustrated in the paper

Targeted demand response for flexible energy communities using clustering techniques

The present study proposes clustering techniques for designing demand response (DR) programs for commercial and residential prosumers. The goal is to alter the consumption behavior of the prosumers within a distributed energy community in Italy. This aggregation aims to: a) minimize the reverse power flow at the primary substation, occuring when generation from solar panels in the local grid exceeds consumption, and b) shift the system wide peak demand, that typically occurs during late afternoon. Regarding the clustering stage, we consider daily prosumer load profiles and divide them across the extracted clusters. Three popular machine learning algorithms are employed, namely k-means, k-medoids and agglomerative clustering. We evaluate the methods using multiple metrics including a novel metric proposed within this study, namely peak performance score (PPS). The k-means algorithm with dynamic time warping distance considering 14 clusters exhibits the highest performance with a PPS of 0.689. Subsequently, we analyze each extracted cluster with respect to load shape, entropy, and load types. These characteristics are used to distinguish the clusters that have the potential to serve the optimization objectives by matching them to proper DR schemes including time of use, critical peak pricing, and real-time pricing. Our results confirm the effectiveness of the proposed clustering algorithm in generating meaningful flexibility clusters, while the derived DR pricing policy encourages consumption during off-peak hours. The developed methodology is robust to the low availability and quality of training datasets and can be used by aggregator companies for segmenting energy communities and developing personalized DR policies

Power optimization in electricity networks

Today, one main challenge that the energy industry faces is the ability to increase energy efficiency. This requires effort from two entities within the energy system – the rule/policy maker from the upper level who creates standards to properly regulate energy usage or energy-trading processes. Another entity is the rule follower, who reacts to the rules or polices wisely to maximize its own benefits taking into consideration economic and quality-of-life issues. This thesis studies three problems to help the rule follower increase its benefit in the electricity market. In the first problem, a power consumer aims to dispatch its power usage over time given different electricity price rates, appliance characteristics, and power limit constraints. We propose an approximate dynamic programming (ADP) algorithm, which works well for small instances. We also propose several scheduling policies for very fast solutions of large scale problems. We show that sorting the requested appliances according to their operating urgency improves the cost. This result allows the power consumer to dispatch the power usage properly and quickly. In the second problem, a demand charge cost is incurred according to the peak load for some large power consumers. A battery system is introduced for peak shaving. Under load uncertainty, we develop a stochastic DP model to solve the problem optimally as well as a Sample Average Approximation (SAA) algorithm for real time implementation. We also introduce several Naive Algorithms for comparison with the DP and SAA algorithm. Finally, we introduce a real time SAA algorithm and test its performance on a data set consisting of 365 days. This algorithm is very effective in terms of generating real time power dispatch plans. In the third problem, we look at the solar power trading problem between a PV farm and the grid operator who imposes complex constraints on the power profile. We propose a Mixed Integer Programming model assuming perfect knowledge of the PV output. Then we relax some of the constraints, and develop a dynamic programming model for the stochastic load problem. We show that a threshold structure battery inventory solution exists for the relaxed problem. We also propose a dynamic programming model with respect to the key constraints from the grid operator

Risk Management using Model Predictive Control

Forward planning and risk management are crucial for the success of any system or business dealing with the uncertainties of the real world. Previous approaches have largely assumed that the future will be similar to the past, or used simple forecasting techniques based on ad-hoc models. Improving solutions requires better projection of future events, and necessitates robust forward planning techniques that consider forecasting inaccuracies. This work advocates risk management through optimal control theory, and proposes several techniques to combine it with time-series forecasting. Focusing on applications in foreign exchange (FX) and battery energy storage systems (BESS), the contributions of this thesis are three-fold. First, a short-term risk management system for FX dealers is formulated as a stochastic model predictive control (SMPC) problem in which the optimal risk-cost profiles are obtained through dynamic control of the dealers’ positions on the spot market. Second, grammatical evolution (GE) is used to automate non-linear time-series model selection, validation, and forecasting. Third, a novel measure for evaluating forecasting models, as a part of the predictive model in finite horizon optimal control applications, is proposed. Using both synthetic and historical data, the proposed techniques were validated and benchmarked. It was shown that the stochastic FX risk management system exhibits better risk management on a risk-cost Pareto frontier compared to rule-based hedging strategies, with up to 44.7% lower cost for the same level of risk. Similarly, for a real-world BESS application, it was demonstrated that the GE optimised forecasting models outperformed other prediction models by at least 9%, improving the overall peak shaving capacity of the system to 57.6%

A predictive optimal dispatch and optimal sizing method for a grid-connected PV-diesel-storage system

Hybrid energy systems are becoming a popular means of exploiting natural sources of energy and increasing electrical efficiency in urban settlements. However, effective implementation of these systems relies on a means of optimally sizing and operating the system to ensure the lifetime costs of the system are minimised. This dissertation addresses the problem of minimising the lifetime costs of a gridconnected hybrid system with diesel generation, photovoltaic array (PV) and an energy storage component. To minimise the operational costs a predictive generator-storage scheduling strategy is proposed. The dispatch strategy seeks to minimise the operational costs by scheduling the generator and storage unit to: 1) Minimise the total energy requested from the grid; 2) Minimise the peak energy requested from the grid; 3) Minimise the fuel used by the generator. The dispatch strategy is developed in two papers. In the first paper a demand prediction algorithm is developed which is required by the proposed predictive dispatch strategy. In the second paper, the actual dispatch strategy for the generator and storage unit is formulated. The dispatch strategy takes the form of an integrated convex optimisation model which, when solved, provides the dispatch strategy for the generator and storage. An optimal sizing method is then developed to take into account the capital costs of the components. The purpose of the optimal sizing method is to balance the trade-off between the increased capital costs incurred by larger PV and storage units and the corresponding decrease in operational costs. The optimal dispatch strategy and sizing method are then tested on an example case study which investigates the possibility of operating a hybrid on the campus of the University of the Witwatersrand

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

Use of vanadium redox flow batteries to store energy for fast charging electric vehicles in gas stations

Dissertação de mestrado integrado em Engenharia MecânicaThe expansion of car traffic and the expected growth of population for the next years have created serious environmental concerns about the dependence on fossil fuels, air pollution and emission of greenhouse gases. In this context, the electric mobility seems to be a good solution for minimize these problems. However, the actual time required to charge the batteries of these vehicles raises other questions about their usefulness. In order to reduce this time, the fast charge method is already available but a high contracted power is needed. On the other hand, the places where it is possible to allocate those chargers is also limited. So, in this work the use of fast charging stations in conjunction with Vanadium Redox Flow Batteries (VRFBs) is assessed. These batteries are charged during low electricity demand periods (at cheap rates) and then supply electricity for the fast charging of Electric Vehicles during electricity peak demand. They may be installed inside deactivated underground gas tanks at gas stations, which are normally located in practical and accessible locations for vehicles. Firstly, a thorough review of the current State of the Art of VRFBs has been done, detailing their genesis, the basic operation of the various existing designs and the current and future prospects of their application. Flow batteries have unique characteristics which make them especially attractive when compared with conventional batteries, such as their long life and their ability to decouple rated maximum power from rated energy capacity, as well as their greater flexibility of shape. Subsequently, a preliminary project of a VRFB system using the philosophy previously described, as well as its economic analysis has been performed. A sensitivity analysis showing the variation of the main output parameters as a function of the input parameters was also presented. Voltage, Current, Power and Pumping Power were predicted and an efficiency around 92% was obtained for a system to charge 26 cars per day. The economic analysis estimated parameters such as the Net Present Value and the Payback Time which have been predicted to be 33 806€ and 9,5 years, respectively, for a lifetime of 20 years.O aumento do tráfego automóvel e o crescimento populacional previsto para os próximos anos tem gerado preocupações ambientais sobre a dependência dos combustíveis fósseis, poluição do ar e emissão de gases de efeito de estufa. Neste contexto a mobilidade elétrica parece ser uma boa opção para minimizar estes problemas, no entanto atualmente o elevado tempo necessário para carregar as baterias destes veículos levanta questões sobre a sua utilidade. O método de carregamento rápido, que tem como objetivo de reduzir este tempo, já está disponível, no entanto é necessária uma elevada potência contratada, sendo os locais onde é possível colocar esses carregadores ainda limitados. Assim, neste trabalho é avaliado o uso de estações de carregamento rápido em conjunto com Baterias de Fluxo Vanádio Redox (VRFBs). Estas baterias são carregadas durante períodos de pouco consumo (a baixas taxas) para posteriormente fornecerem energia para carregar Veículos Elétricos durante horas de maior consumo de eletricidade. Estas baterias podem ser instaladas dentro de tanques de combustível subterrâneos desativados existentes nos postos de abastecimento de combustível, os quais estão normalmente situados em locais de fácil acesso a veículos. Em primeiro lugar, realizou-se uma revisão do estado da arte das VRFBs, detalhando a sua génese, o modo de operação de várias configurações existentes e as perspectivas atuais e futuras da sua aplicação. As baterias de fluxo possuem características que as tornam especialmente atrativas em comparação com as baterias convencionais, como o longo ciclo de vida, a independência entre potência máxima e capacidade, assim como a sua grande flexibilidade de forma. Realizou-se ainda um projeto preliminar de uma VRFB de acordo com o conceito anteriormente descrito, que incluiu a sua análise económica. Apresenta-se ainda uma análise de sensibilidade que mostra a variação dos principais parâmetros de output em função da variação dos parâmetros de input. A tensão, potência, corrente e potência de bombagem foram simuladas, tendo-se obtido um rendimento de 92% para um sistema que permite carregar 26 carros por dia. A análise de custos incluiu a estimativa de parâmetros como o Valor Atual Líquido (33 806€) e o Tempo de Recuperação (9,5 anos), para um ciclo de vida considerado de 20 anos