Control of Energy Storage

In the attempt to tackle the issue of climate change, governments across the world have agreed to set global carbon reduction targets. [...

On-line adjustment of battery schedules for supporting LV distribution network operation

Increasing domestic demand for electric energy is expected to put significant strain on the existing power distribution networks. In order to delay or prevent costly network reinforcement, some UK Distribution Network Operators (DNOs) are investigating the use of Battery Energy Storage Solutions (BESS), or other demand response systems, in the Low-Voltage (LV) power distribution networks to reduce peak demand. In most cases the control strategies, and metrics of success, are evaluated on a half-hourly basis and so sub-half-hourly (i.e. minute by minute) variations in demand are not effectively addressed. In this work, a closed-loop optimisation methodology is proposed that adjusts the pre-scheduled charging profile of a BESS in a sub-half-hourly manner in order to improve network operation whilst maintain the same average net energy flow over the half-hour period. This new approach guarantees that the BESS follows its predetermined half-hourly schedule, yet voltage and power imbalance, network losses, and feeder overloading are additionally mitigated through sub-half-hourly control actions. For validation, this paper presents a case study based on the real BESS installed in Bracknell as part of Thames Valley Vision project with Scottish and Southern Energy Power Distribution (SSE-PD) evaluated on the IEEE LV test case feeder model

Online control algorithm for sub-half-hourly operation of LV-connected energy storage device owned by DNO

Change in consumer behaviour through uptake of Low Carbon Technologies is likely to put existing distribution networks under strain and worsen the operational requirements of the network. Deployment of energy storage and power electronics is a feasible alternative to traditional network reinforcement. This paper presents two control algorithms used with an energy storage device deployed as part of New Thames Valley Vision Project. The two algorithms are aimed at 1) equalising phase loading with correction of power factor and 2) providing voltage support with Additive Increase Multiplicative Decrease algorithm for active and reactive power control

Distributed Energy Storage Control for Dynamic Load Impact Mitigation

The future uptake of electric vehicles (EV) in low-voltage distribution networks can cause increased voltage violations and thermal overloading of network assets, especially in networks with limited headroom at times of high or peak demand. To address this problem, this paper proposes a distributed battery energy storage solution, controlled using an additive increase multiplicative decrease (AIMD) algorithm. The improved algorithm (AIMD+) uses local bus voltage measurements and a reference voltage threshold to determine the additive increase parameter and to control the charging, as well as discharging rate of the battery. The used voltage threshold is dependent on the network topology and is calculated using power flow analysis tools, with peak demand equally allocated amongst all loads. Simulations were performed on the IEEE LV European Test feeder and a number of real U.K. suburban power distribution network models, together with European demand data and a realistic electric vehicle charging model. The performance of the standard AIMD algorithm with a fixed voltage threshold and the proposed AIMD+ algorithm with the reference voltage profile are compared. Results show that, compared to the standard AIMD case, the proposed AIMD+ algorithm further improves the network’s voltage profiles, reduces thermal overload occurrences and ensures a more equal battery utilisation

Time-of-use and time-of-export tariffs for home batteries: Effects on low voltage distribution networks

Time-of-use electricity tariffs are gradually being introduced around the world to expose consumers to the time-dependency of demand, however their effects on peak flows in distribution networks, particularly in areas with domestic energy storage, are little understood. This paper presents investigations into the impact of time-of-use and time-of-export tariffs in residential areas with various penetrations of battery storage, rooftop solar PV, and heat pumps. By simulating battery operation in response to high resolution household-level electrical and thermal demand data, it is found that home batteries operating to maximise cost savings in houses signed up to time-dependent tariffs cause little reduction in import and export peaks at the low voltage level, largely because domestic import and export peaks are spread out over time. When operating to maximise savings from the first three-tier time-of-use tariff introduced in the UK, batteries could even cause increases in peak demand at low voltage substations, if many batteries in the area commence charging at the start of the overnight off-peak price band. Home batteries operating according to time-dependent electricity tariffs significantly miss out on the potential peak shaving that could otherwise be achieved through dedicated peak shaving incentives schemes and smarter storage control strategies

Control and schedule adjustments of battery based energy storage in low-voltage distribution networks

British Distribution Network Operators (DNOs) are facing challenges due to the energy sectors transitioning into a low carbon economy. This thesis aims to present novel methods to aid DNOs in operating their Low-Voltage (LV) networks despite this ongoing transition and its entailed challenges. The presented methods are realised with the use of Battery Energy Storage Solutions (BESS) and they develop BESS energy management algorithms whilst focusing on communication regimes and sub-half-hourly volatility in demand. Consequently, improving LV network operation mainly considers the reduction of peak power flow, but also includes reducing energy losses, voltage deviation, the magnitude of neutral currents and phase unbalance. Without these methods, DNOs would have to rely on traditional network reenforcements so that LV networks are kept within statutory voltage bands, for example. Extending current literature with methods to control a single energy resource and a distributed BESS - whilst considering requirements for communication systems that effect BESS control - is the main contribution of this thesis. The BESS control algorithm developed in this thesis is designed to incorporate half-hourly forecasts and sub-half-hourly load volatility. Resulting key network parameters and their interplay are identified and daily load peaks, caused by load volatility, could be reduced by an average of 3.8kW(from 45kW). Methods are developed and address challenges for controlling a single BESS. Neglected challenges are addressed in the subsequent BESS control methods where a desynchronised Multi-Agent Network (MAS) and communication-less BESS control fill this gap. Results show how internal algorithm behaviour changes when desynchronising the communication environment, but without impacting the global performance of the distributed BESS. Also, realtime performance of the communication less control algorithm is studied on different basis to show how effects from uncoordinated Low-Carbon Technologies (LTCs) like Electric Vehicle (EV) charging, can be successfully mitigated. All objectives aligning with the aforementioned achievements have been met and the comparable storage control techniques in literature are either met or exceeded in performance when subjected to the available datasets