Power System Steady-State Analysis with Large-Scale Electric Vehicle Integration

It is projected that the electric vehicle will become a dominant method of transportation within future road infrastructure. Moreover, the electric vehicle is expected to form an additional role in power systems in terms of electrical storage and load balancing. This paper considers the latter role of the electric vehicle and its impact on the steady-state stability of power systems, particularly in the context of large-scale electric vehicle integration. The paper establishes a model framework which examines four major issues: electric vehicle capacity forecasting; optimization of an object function; electric vehicle station siting and sizing; and steady-state stability. A numerical study has been included which uses projected United Kingdom 2020 power system data with results which indicate that the electric vehicle capacity forecasting model proposed in this paper is effective to describe electric vehicle charging and discharging profiles. The proposed model is used to establish criteria for electric vehicle station siting and sizing and to determine steady-state stability using a real model of a small-scale city power system

On the performance evaluation of lithium-ion battery systems for dynamic load functions in warship hybrid power and propulsion systems

Battery technology has developed to a juncture where high power and high energy density characteristics can be exploited for a common use battery energy storage system (ESS) for warship power systems to improve system steady state and dynamic performance. A critical review of previous research has exposed a lack of knowledge in performance assessment of battery ESS to operate as power reserve, to load level generator sets and supply laser directed energy weapons (LDEW) in a warship hybrid power and propulsion system. This research explores the performance impact of using a battery ESS in a candidate hybrid power and propulsion system. A simulation model of a lithium-ion nickel manganese cobalt based ESS was developed and validated against high rates of charge and discharge. Three system models were developed to explore the steady state, quasi-steady state and dynamic performance of the candidate power system when the battery is integrated. Three key investigations were conducted using the respective system models. The first explored the effects of ESS on the candidate power system performance when the ESS is operated as power reserve. Analysis showed that a 40% reduction in exhaust greenhouse gas (GHG) emissions was potentially achievable from the candidate warship compared to conventional operating practice. The second explored power system performance when operating the ESS operates to load level a diesel generator under quasi-steady state conditions. A 2% droop limit is suggested to mitigate against adverse quality of power supply (QPS) conditions for electrical consumers. The third investigation, and key contribution to the field of naval power systems, explored the impact of LDEW demands on the transient response of the ESS and system quality of power supply. The research findings show that the battery ESS is capable of high rates of fire for extended periods subject to state of charge operating limitations. To mitigate against adverse QPS conditions and provide operators with a realistic operating envelope to power the laser with the battery ESS, it is recommended that the power limit of the laser load should be 1.75 MW peak power

Energy management of hybrid and battery electric vehicles

This work focuses on improving the fuel economy of parallel Hybrid Electric Vehicles (HEVs) and dual-motor Electric Vehicles (EVs) through energy management strategies. Both vehicle models have two propulsion branches, each powering a separate axle: An engine and an electric motor in the HEV and two electric motors in the EV. This similarity in the vehicle models emphasises the need for similar energy management solutions. In Part Energy Management of HEVs of this thesis, a high-fidelity parallel Through-The-Road (TTR) HEV model is developed to study and test conventional control strategies. The traditional control strategies serve as a guide for developing novel heuristic control strategies. The Equivalent Consumption Minimisation Strategy (ECMS) is an optimisation-based control strategy used as the benchmark in this part of the work. A family of rule-based energy management strategies is proposed for parallel HEVs, including the Torque-levelling Threshold-changing Strategy (TTS) and its simplified version, the Simplified Torque-levelling Threshold-changing Strategy (STTS). The TTS applies a concept of torque-levelling, which ensures the engine works efficiently by operating with a constant torque as the load demand crosses a certain threshold, unlike the load-following approach commonly used. However, the TTS requires finely tuned constant torque and threshold parameters, making it unsuitable for real-time applications. To address this, two feedback-like updating laws are incorporated into the TTS to determine the constant torque and threshold online for real-time applications. Real-time versions of these strategies, Real-time Torque-levelling Threshold-changing Strategy (RTTS) and Real-time Simplified Torque-levelling Threshold-changing Strategy (RSTTS) are developed using a novel Driving Pattern Recognition (DPR) algorithm. The effectiveness of the RTTS is demonstrated by implementing it on a high-fidelity parallel hybrid passenger car and benchmarking it against ECMS. In Part Energy Management of EVs of the thesis, a low-fidelity model of a novel EV powertrain with two electric propulsion systems, one at each axle, has been developed to study and test its energy management with one of the main conventional optimal control methods, Dynamic Programming (DP). The EV model uses two differently sized traction motors at the front and rear axles. The thermal dynamics of the utilised Permanent Magnet Synchronous Motors (PMSMs) are studied. DP is first implemented onto the Baseline model that does not include any PMSM thermal dynamics, referred to as the Baseline DP, which acts as a benchmark since it is the conventional case. The thermal dynamics of the traction motors are then introduced in the second DP problem formulation, referred to as the Thermal DP, which is compared against the Baseline DP to evaluate the possible benefits of energy efficiency by the more informed energy management optimisation formulation. The best method is chosen to include these thermal dynamics in the overall energy management control strategy without significantly compromising computational time.Open Acces

Bi-level Planning Model for Optimal Battery Energy Storage Allocation Considering Optimal Daily Scheduling Using Mixed-Integer Particle Swarm Optimization

This paper proposes a bi-level optimization (BLO) approach for optimal battery energy storage system (BESS) allocation (OBA) in distribution network (DN) considering optimal BESS daily scheduling (OBDS). The objective is to obtain the best locations and daily scheduling of BESSs that minimize total energy loss in DNs. In the upper-level of the proposed BLO method, the OBA is solved by mixed-integer particle swarm optimization (MIPSO). Meanwhile, the OBDS is solved as a sub-problem by particle swarm optimization in the lower-level of BLO. The proposed BLO based OBA considering OBDS algorithm had been tested with IEEE 33-bus radial distribution test system using load profile of Thai’s power system during summer, winter, and rainy seasons comparing to mixed-integer genetic algorithm (MIGA) method. The simulation result shown that the proposed lower-level OBDS can efficiently minimize the total daily loss by BESS scheduling. Moreover, the proposed algorithm can also achieve the optimal placement of BESS

Economic benefits of pumped storage units in probabilistic production costing

Imperial Users onl

Modelling of a stand alone photovoltaic system with dedicated hybrid battery energy storage system

Includes abstract.Includes bibliographical references.The purpose of this thesis project was to model and simulate a stand-alone photovoltaic (PV) plant that utilized the maximum power point tracking (MPPT) technique and included a hybrid battery energy storage system (BESS). The model consisted of five main components namely; the photovoltaic module, maximum power point tracking technique, hybrid battery energy storage system, controller and load

A Study of the Impact of Integrating Energy Storage and PV Systems into Domestic Distribution Networks in Ireland

La tesi riguarda uno studio di livellamento del carico durante l'arco della giornata tramite l'uso di sistemi di accumulo elettrochimico. Sono stati utilizzati come dati dei valori di potenze assorbite da carichi residenziali provenienti da uno studio di smart metering Irlandese. Tramite Matlab e Simulink è stato creato un modello e sono stati considerati degli scenari con i quali si sono calcolate le perdite in linea e la miglior allocazione delle batterie per minimizzare le perdite stesse

A Case Study on Application of Fuzzy Logic based Controller for Peak Load Shaving in a Typical Household\u27s Per Day Electricity Consumption

The cost of electricity for consumers depends on the cost of generation, transmission, and distribution of power. The electrical load consumed by consumers per day is not constant throughout the day. The utilities must be capable of meeting the load demand, which means they must have enough electricity generation potential and necessary infrastructure. This cost is significant. However, the revenue they generate will only be for the actual use of electricity by the consumers. In general, the electrical power generation is done in stages, always generating a base load. As demand changes throughout the day, additional stages of power generation are brought online to meet the changes in demand. This approach of management is known as supply-side management. Theoretically, if it is possible to manage the load such that there is lower peak demand and the difference between peak load and base load were minimized, the generation capability and grid infrastructure required to provide reliable power would be reduced resulting in lower costs for utility companies and ultimately consumers. This management strategy is referred to as demand-side management or demand response. In this research, a small-scale smart grid is modeled in Simulink to mimic the electrical grid. A Smart controller based on fuzzy logic is developed to control charging and discharging of an electric vehicle battery to provide extra power during peak times and to act as load (storing energy) during off-peak time to provide a more manageable and balanced load as seen by the grid. A comparative study is presented of electricity consumption throughout the day with or without the smart controller. The results show the significant reduction in peak demand, much smoother load curve for the grid, and a decrease in per kilowatt cost of electricity for the given day when newer pricing structures are applied

Grid Scale Battery Energy Storage Investment Potential - Analysis and Simulations of Frequency Control Markets in Germany and the UK

The need for energy storages in future power systems is acknowledged both in literature and in industry. Simultaneously battery energy storage technologies, especially Lithium-ion, are seen technologically relatively mature with favorable cost development. Whereas frequency control markets provide exploitable commercial and technical framework for battery investment. Nevertheless, true commercial viability is still uncertain in leading European markets in Germany and the UK. The purpose of the study was to provide complete and comparative market analysis and demonstrated prospective investment profitability outcomes for grid scale battery energy storages in Germany and the UK. In addition, the study aimed to show required conditions for desired investment performances. The study explored investment potential in primary frequency control market in Germany and enhanced frequency response market in the UK by analyzing market attractiveness from multiple aspects. The countries were ranked based on the analyzed aspects by Analytical Hierarchy Process. Finally, financial Monte Carlo investment simulations with revenue and cost uncertainties were performed. Simulations also provided required conditions for profitability. Analyzed data was based on historical market data, performed online market research and literature. Key findings of the study revealed that the chosen markets form suitable commercial framework for battery investments, but Germany shows clearly higher potential. However, the potential was questionable since both markets face significant challenges especially in financial sense. The concerns were confirmed by the simulations which suggested around 1–5 % and -3–3 % internal rate of return levels for Germany and the UK respectively. In addition, reaching 6 % return was seen very challenging whilst over 10 % return levels seemed unrealistic in the UK and extremely optimistic for Germany. The overall conclusion was that battery energy storage investment in either of the markets cannot currently be justified primarily by financial returns but needs strategic support.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format