Optimizing daily operation of battery energy storage systems under real-time pricing schemes

Modernization of electricity networks is currently being carried out using the concept of the smart grid; hence, the active participation of end-user consumers and distributed generators will be allowed in order to increase system efficiency and renewable power accommodation. In this context, this paper proposes a comprehensive methodology to optimally control lead-acid batteries operating under dynamic pricing schemes in both independent and aggregated ways, taking into account the effects of the charge controller operation, the variable efficiency of the power converter, and the maximum capacity of the electricity network. A genetic algorithm is used to solve the optimization problem in which the daily net cost is minimized. The effectiveness and computational efficiency of the proposed methodology is illustrated using real data from the Spanish electricity market during 2014 and 2015 in order to evaluate the effects of forecasting error of energy prices, observing an important reduction in the estimated benefit as a result of both factors: 1) forecasting error and 2) power system limitations

Parallel statistical model checking for safety verification in smart grids

By using small computing devices deployed at user premises, Autonomous Demand Response (ADR) adapts users electricity consumption to given time-dependent electricity tariffs. This allows end-users to save on their electricity bill and Distribution System Operators to optimise (through suitable time-dependent tariffs) management of the electric grid by avoiding demand peaks. Unfortunately, even with ADR, users power consumption may deviate from the expected (minimum cost) one, e.g., because ADR devices fail to correctly forecast energy needs at user premises. As a result, the aggregated power demand may present undesirable peaks. In this paper we address such a problem by presenting methods and a software tool (APD-Analyser) implementing them, enabling Distribution System Operators to effectively verify that a given time-dependent electricity tariff achieves the desired goals even when end-users deviate from their expected behaviour. We show feasibility of the proposed approach through a realistic scenario from a medium voltage Danish distribution network

Novel probabilistic optimization model for lead-acid and vanadium redox flow batteries under real-time pricing programs

The integration of storage systems into smart grids is being widely analysed in order to increase the flexibility of the power system and its ability to accommodate a higher share of wind and solar power. The success of this process requires a comprehensive techno-economic study of the storage technology in contrast with electricity market behaviour. The focus of this work is on lead-acid and vanadium redox flow batteries. This paper presents a novel probabilistic optimization model for managing energy storage systems. The model is able to incorporate the forecasting error of electricity prices, offering with this a near-optimal control option. Using real data from the Spanish electricity market from the year 2016, the probability distribution of forecasting error is determined. The model determines electricity price uncertainty by means of Monte Carlo Simulation and includes it in the energy arbitrage problem, which is eventually solved by using an integer-coded genetic algorithm. In this way, the probability distribution of the revenue is determined with consideration of the complex behaviours of lead-acid and vanadium redox flow batteries as well as their associated operating devices such as power converters

Day-ahead optimal battery operation in islanded hybrid energy systems and its impact on greenhouse gas emissions

This paper proposes a management strategy for the daily operation of an isolated hybrid energy system (HES) using heuristic techniques. Incorporation of heuristic techniques to the optimal scheduling in day-head basis allows us to consider the complex characteristics of a specific battery energy storage system (BESS) and the associated electronic converter efficiency. The proposed approach can determine the discharging time to perform the load peak-shaving in an appropriate manner. A recently proposed version of binary particle swarm optimization (BPSO), which incorporates a time-varying mirrored S-shaped (TVMS) transfer function, is proposed for day-ahead scheduling determination. Day-ahead operation and greenhouse gas (GHG) emissions are studied through different operating conditions. The complexity of the optimization problem depends on the available wind resource and its relationship with load profile. In this regard, TVMS-BPSO has important capabilities for global exploration and local exploitation, which makes it a powerful technique able to provide a high-quality solution comparable to that obtained from a genetic algorithm

Interruption reduction at substations using battery energy storage systems.

Masters Degree. University of KwaZulu-Natal, Durban.Reliable electrical power supply is vital in the modern society and electrical distribution utilities are responsible for ensuring continuity of supply. South Africa is experiencing a rapid increase in electrical power demand; with the number of people requiring electrification growing continuously. Eskom’s capacity to generate excess electricity is completely used up as the currently installed transformers are of a certain fixed rating and cannot accommodate the rapid continuous growth, resulting in the utility not being able to meet the power demand. The concept of load shedding is utilized when supply cannot meet demand due to certain system constraints and demand reduction is required; it is also regarded as a last resort action taken to prevent the collapse of the power system and protect the current electrical power equipment connected to the system. The constraints are mainly due to the incapability to store the power at any point in the supply, traditionally electrical power generation plants typically produce more energy than necessary to ensure adequate power quality at the points of transmission and distribution as a large percentage of the energy is lost in the power station as waste heat and even more losses occur at the power lines when the generated is transmitted for use; thus raising a need to implement a system to save as much of the discarded energy in between the points of the life-cycle of electricity such as battery energy storage systems (BESS). BESS is useful for its prompt capacity to adjust power well as the characteristics of storage and supply capability. The utilization of BESS for the reduction of network power loss and management of network congestion is the key factor to realize the optimal operation of distribution networks as it can store excess power that can be later utilized when there’s a shortage in the system. In this dissertation, the integration of BESS into electrical Distribution systems is investigated, with the objective to reduce the power supply interruptions that occur at the substations (planned/unplanned) and know-how BESS can be used to improve the performance of a distribution system. The proposed methodology consists of two main parts. The first one is of design and simulation of a balanced substation; It’s important to ensure that the substation is operating within its specifications as a standalone before any external features are added to improve the already existing adequate performance. And separately, a BESS with a control method for State of Charge (SoC) for the battery considering the network power loss during both grid and off-grid operation to ensure smooth BESS operation without compromising the voltage regulation performance of the network; as the basis of the investigation, consecutively. The second part consists of integrating the two models (Substation & BESS) and conducting simulation studies to obtain unique scenario-based outcomes. The optimal placement of BESS is investigated as the efficiency of integrating it into large-scale distribution networks depends on it. The substation and BESS are modelled and simulated using MATLAB Simulink to verify the effectiveness of the proposed methodology and based on the research it is evident that BESS integrated distribution systems solutions to overcome shortage created by load shedding or any other interruptions (planned/unplanned) are the best way to go in maintaining continuity of supply to customers.List of figures and tables are on page viii-ix

Improving Performance Assessment for Technologies of Energy Transition: Emissions, Economics, and Policy Implications

University of Minnesota Ph.D. dissertation. August 2018. Major: Natural Resources Science and Management. Advisors: Timothy Smith, Elizabeth Wilson. 1 computer file (PDF); vii, 104 pages.Global climate change requires immediate actions to mitigate emissions from energy related sectors. Specifically, the electricity system plays a pivotal role in achieving the global emission reduction goals that many countries have publicly committed to. In the United States (U.S.), energy policies have focused on increasing electricity production from renewables, decreasing electricity consumption by improving energy efficiency, and shifting demand by using energy storage technology. This dissertation explores the specific challenges and information gaps that confront practitioners in three separate case studies, consequently contributing to electricity system and energy policy literature. It is the hope of the author that information provided helps to inform policy makers, electricity system operators, and private investors toward critical transition and transformation of the U.S. energy system. The studies, taking the form of independent chapters, are summarized as follows. The first study presents an improved methodology for estimating the marginal emission factors (MEFs) of electricity generation in the Midcontinent Independent System Operator (MISO) system. Findings highlight the importance of including emitting and nonemitting resources in MEFs calculation in regions with high and growing renewables penetration and compare this approach to competing conventional approaches within the context of energy storage technologies. The second study demonstrates a multi-regional energy and emissions assessment of the ground source heat pump (GSHP) technology in comparison to the conventional heating and cooling technologies in residential houses. Findings indicate that applying EFs with higher spatial and temporal resolutions and using MEFs instead of average emission factors (AEFs) both give more accurate emission estimates. The third study assesses economics and emissions of grid-scale battery storage that arbitrages as a price taker in the MISO wholesale electricity market. Findings demonstrate specific locations where battery storage might initially be most profitable under historical pricing dynamics and reveal the heterogeneity in storage’s economics and emissions throughout the MISO grid

Control of frequency in future power systems

Future power systems will face a significant challenge due to the reduced stability of frequency. The reduction of inertia drives this challenge due to the increasing level of power electronics connected to renewable energy sources. In this thesis, new control techniques,such as a new secondary frequency control, a control of a population of water heaters(WHs), and a control of a population of battery energy storage systems (BESSs), are studied. A fuzzy logic-based secondary frequency controller was developed to supplement the conventional frequency control in large synchronous generators. This controller is suitable for the provision of mandatory frequency response in the Great Britain (GB) power system, where an additional 10% power output for primary response and 10% for secondary response are required within ten seconds and thirty seconds respectively. The controller was demonstrated using a simplified GB power system and a multi-machine benchmark power system. The results showed that, following a disturbance, the controller improved frequency deviation and error compared to the conventional PI controller. Thus, the controller provides a stable frequency control in future power systems. A hierarchical control of a population of WHs and BESSs was used to provide frequency response services. This was based on two decision layers. The aggregator layer receives the states of WHs/BESSs and sends a command signal to each WH/BESS control layer. The hierarchical control enables the aggregator to choose the number of controllable WHs/BESSs and set the desired amount of responses to offer different frequency response services. As a result, it reduces the uncertainty associated with the response of the population during a frequency event. The WH/BESS controller provides a response based on the last command signal from the aggregator, the value of frequency deviation (ΔF) and the level of the water temperature or BESS state of charge (SoC). The WH/BESS controller provides a response even when a failure occurs in the communication with the aggregator control layer. The WH/BESS controller handles both negative and positive ΔF. Hence, the aggregated loads participate in both low and high frequency responses. The response of the population of BESSs goes from the highest to lowest SoC when the frequency falls and from the lowest to highest SoC when it rises. The response from WHs is from highest to lowest water temperature when the frequency drops. Thus, this reduces the risk of a simultaneous power change in a large number of controllable loads at the same time, which, in turn, reduces the impact. The dynamic behaviour of a population of WHs/BESSs was modelled based on the Markov chain to allow the aggregator to offer different frequency response services. A Markov-based model was also used to evaluate the effective capacity of aggregated WHs/BESSs during the frequency event. The Markov-based model was demonstrated on a simplified GB power system and the South-East Australian power system, considering different aggregation case studies