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

Optimal electric vehicle scheduling : A co-optimized system and customer perspective

Electric vehicles provide a two pronged solution to the problems faced by the electricity and transportation sectors. They provide a green, highly efficient alternative to the internal combustion engine vehicles, thus reducing our dependence on fossil fuels. Secondly, they bear the potential of supporting the grid as energy storage devices while incentivizing the customers through their participation in energy markets. Despite these advantages, widespread adoption of electric vehicles faces socio-technical and economic bottleneck. This dissertation seeks to provide solutions that balance system and customer objectives under present technological capabilities. The research uses electric vehicles as controllable loads and resources. The idea is to provide the customers with required tools to make an informed decision while considering the system conditions. First, a genetic algorithm based optimal charging strategy to reduce the impact of aggregated electric vehicle load has been presented. A Monte Carlo based solution strategy studies change in the solution under different objective functions. This day-ahead scheduling is then extended to real-time coordination using a moving-horizon approach. Further, battery degradation costs have been explored with vehicle-to-grid implementations, thus accounting for customer net-revenue and vehicle utility for grid support. A Pareto front, thus obtained, provides the nexus between customer and system desired operating points. Finally, we propose a transactive business model for a smart airport parking facility. This model identifies various revenue streams and satisfaction indices that benefit the parking lot owner and the customer, thus adding value to the electric vehicle --Abstract, page iv

Reforming electricity rates to enable economically competitive electric trucking

The imperative to decarbonize long-haul, heavy-duty trucking for mitigating both global climate change as well as air pollution is clear. Given recent developments in battery and ultra-fast charging technology, some of the prominent barriers to electrification of trucking are dissolving rapidly. Here we shed light on a significant yet less-understood barrier, which is the general approach to retail electricity pricing. We show that this is a near term pathway to 0.06/kWh charging costs that will make electric trucking substantially cheaper than diesel. This pathway includes (i) reforming demand charges to reflect true, time-varying system costs; (ii) avoiding charging during a few specific periods (<45 h in a year) when prices are high; and (iii) achieving charging infrastructure utilization of 33% or greater. However, without reforming demand charges and low utilization of charging infrastructure, charging costs more than quadruple (to 0.28/kWh). We also illustrate that a substantial share of current trucking miles within select large regions of the United States can be reliably electrified without constraining electricity generation capacity as it exists today. Using historical hourly electricity price and load data for last 10 years and future projections in Texas and California, we show that electricity demand is at least 10% lower than yearly peak demand for at least 15 h on any given day. In sum, with electricity rates that closely reflect actual power system costs of serving off-peak trucking load, we show that electric trucks can provide overwhelming cost savings over diesel trucks. For reference, at diesel prices of $3.16/gal and charging costs of$0.06/kWh (inclusive of amortized charging station infrastructure costs), an electric truck's fuel cost savings are $251 000 (NPV), providing net savings of$61 000 (18% of lifetime diesel fuel cost) over the truck's lifetime at battery price of $170/kWh, or up to$148 000 (44% of lifetime diesel fuel cost) at a battery price of $100/kWh (figure 1)

Integration of Energy Storage into a Future Energy System with a High Penetration of Distributed Photovoltaic Generation

Energy storage units (ESU) are increasingly used in electrical distribution systems because they can perform many functions compared with traditional equipment. These include peak shaving, voltage regulation, frequency regulation, provision of spinning reserve, and aiding integration of renewable generation by mitigating the effects of intermittency. As is the case with other equipment on electric distribution systems, it is necessary to follow appropriate methodologies in order to ensure that ESU are installed in a cost-effective manner and their benefits are realized. However, the necessary methodologies for integration of ESU have not kept pace with developments in both ESU and distribution systems. This work develops methodologies to integrate ESU into distribution systems by selecting the necessary storage technologies, energy capacities, power ratings, converter topologies, control strategies, and design lifetimes of ESU. In doing so, the impact of new technologies and issues such as volt-VAR optimization (VVO), intermittency of photovoltaic (PV) inverters, and the smart PV inverter proposed by EPRI are considered. The salient contributions of this dissertation follow. A unified methodology is developed for storage technology selection, storage capacity selection, and scheduling of an ESU used for energy arbitrage. The methodology is applied to make technology recommendations and to reveal that there exists a cost-optimal design lifetime for such an ESU. A methodology is developed for capacity selection of an ESU providing both energy arbitrage and ancillary services under a stochastic pricing structure. The ESU designed is evaluated using ridge regression for price forecasting; Ridge regression applied to overcome numerical stability and overfitting issues associated with the large number of highly correlated predictors. Heuristics are developed to speed convergence of simulated annealing for placement of distributed ESU. Scaling and clustering methods are also applied to reduce computation time for placement of ESU (or any other shunt-connected device) on a distribution system. A probabilistic model for cloud-induced photovoltaic (PV) intermittency of a single PV installation is developed and applied to the design of ESU

Efficient Energy Optimization for Smart Grid and Smart Community

The electric power industry has undergone significant changes in response to the environmental concerns during the past decades. Nowadays, due to the integration of different distributed energy systems in the smart grid, the balancing between power generation and load demand becomes a critical problem. Specifically, due to the intermittent nature of renewable energy sources (RESs) , power system optimization becomes significantly complicated. Due to the uncertain nature of RESs, the system may fail to ensure the power quality which may cause increased operating costs for committing costly reserve units or penalty costs for curtailing load demands. This dissertation presents three projects to study the optimization and control for smart grid and smart community. First, optimal operation of battery energy storage system (BESS) in grid-connected microgrid is studied. Near optimal operation/allocation of the BESS is investigated with the consideration of battery lifetime characteristics. Approximate dynamic programming (ADP) is proposed to solve optimal control policy for time-dependent and finite-horizon BESS problems and performance comparison is done with classical dynamic programming approach. The results show that the ADP can optimize the system operation under different scenarios to maximize the total system revenue. Second, optimal operation of the BESS in islanded microgrid is also studied. Specifically, a new islanded microgrid model is formulated based on Markov decision process. A computationally efficient ADP approach is proposed to solve this energy optimization problem, and achieve near minimum operational cost efficiently. Simulation results show that the proposed ADP can achieve 100% and at least 98% of optimality for deterministic and stochastic case studies, respectively. The performance of the proposed ADP approach also achieved 18:69 times faster response than that of the traditional DP approach for 0:5 million of data samples. Third, a demand side management technique is proposed for the optimization of residential demands with financial incentives. A new design of comfort indicator is proposed considering both thermal and other electric appliances based on consumers’ comfort level. The proposed approach is compared with two existing demand response approaches for both 10-houses and 100-houses simulation studies. For both cases, the proposed approach outperformed the existing approaches in terms of reward incentives and comfort levels

Optimal energy management of a campus microgrid considering financial and economic analysis with demand response strategies

An energy management system (EMS) was proposed for a campus microgrid (&micro;G) with the incorporation of renewable energy resources to reduce the operational expenses and costs. Many uncertainties have created problems for microgrids that limit the generation of photovoltaics, causing an upsurge in the energy market prices, where regulating the voltage or frequency is a challenging task among several microgrid systems, and in the present era, it is an extremely important research area. This type of difficulty may be mitigated in the distribution system by utilizing the optimal demand response (DR) planning strategy and a distributed generator (DG). The goal of this article was to present a strategy proposal for the EMS structure for a campus microgrid to reduce the operational costs while increasing the self-consumption from green DGs. For this reason, a real-time-based institutional campus was investigated here, which aimed to get all of its power from the utility grid. In the proposed scenario, solar panels and wind turbines were considered as non-dispatchable DGs, whereas a diesel generator was considered as a dispatchable DG, with the inclusion of an energy storage system (ESS) to deal with solar radiation disruptions and high utility grid running expenses. The resulting linear mathematical problem was validated and plotted in MATLAB with mixed-integer linear programming (MILP). The simulation findings demonstrated that the proposed model of the EMS reduced the grid electricity costs by 38% for the campus microgrid. The environmental effects, economic effects, and the financial comparison of installed capacity of the PV system were also investigated here, and it was discovered that installing 1000 kW and 2000 kW rooftop solar reduced the GHG generation by up to 365.34 kg CO2/day and 700.68 kg CO2/day, respectively. The significant economic and environmental advantages based on the current scenario encourage campus owners to invest in DGs and to implement the installation of energy storage systems with advanced concepts

Maximising the benefit of distributed wind generation through intertemporal Active Network Management

The role of distribution networks is changing. There is a significant drive, influenced by climate change and security of supply issues, to move electricity generation towards renewable technologies. This is leading to an increase in demand for renewable generation connections at the distribution network level and putting pressure on distribution network operators to change the 'fit-and-forget' philosophy of network operation to include more active approaches. In the UK this is seen through the development of Active Network Management schemes which manage distributed generation in real-time, applying constraints when required to maintain network limits. In parallel, technologies have been developed that are capable of providing intertemporal flexibility, of which two particular examples are energy storage and flexible demand. The objective of the thesis is to answer the questions: How can energy storage and flexible demand be scheduled in a second-generation Active Network Management scheme? And how should they be operated to gain most benefit from distributed wind generation? To answer these questions, the thesis develops and uses tools to study the optimisation of second-generation Active Network Management schemes including intertemporal technologies. The tools developed include a Dynamic Optimal Power Flow algorithm for management of energy storage and flexible demand. The thesis provides the first fully flexible model of energy storage in this context, the first implementation of principles-of-access in an optimal power flow, and the first detailed study of the role of energy storage and flexible demand in managing thermal limits and reducing curtailment of distributed wind generation. The thesis also develops the theory of Dynamic Locational Marginal Pricing based on the economic information contained in an optimal solution to a Dynamic Optimal Power Flow. The thesis shows this to be a useful way of understanding the economic impact of intertemporal flexibility and monetary flows in markets which contain them. The thesis goes on to provide a detailed report of the application of Dynamic Optimal Power Flow and Dynamic Locational Marginal Pricing to an islanded Active Network Management scheme currently in deployment in the UK. This highlights the ability of the tools developed to contribute to future projects. A conclusions of the thesis is that DOPF provides a useful method of scheduling flexible devices such as energy storage and power systems. It takes full account of network constraints and limitations, and as applied in this thesis, the most complete models of the intertemporal effects of energy storage and flexible demand to date. The studies contained in the thesis show that energy storage and flexible demand can increase the benefit of distributed wind generation in Active Network Management by minimising curtailment and transferring generated electricity to periods during which the energy has greatest value in offsetting expensive, fossil fuel based generation. The thesis notes the importance of a useful definition of the 'benefit' of wind generation in terms of global objectives such as minimising emissions rather than interim objectives such as maximising generation from renewables. The thesis discusses the importance of losses in energy storage, and the relationship of storage and network losses with curtailment of wind and the lost opportunity of generating electricity. In terms of losses, the extension of existing economic analysis methods leads to the result that flexibility will only operate between time-steps where the ratio of prices is greater than the round-trip losses of the store. Within this constraint, effective use of energy storage is shown to result from regular charging and discharging. The comparison between energy storage and flexible demand shows that where there are few losses associated with flexibility in demand it is significantly more successful than energy storage at mitigating the effects of variability in wind. The final study of an islanded distribution network with wind curtailment, concludes that energy storage is less effective that flexible demand at reducing wind curtailment, but can provide benefit through management of peak demand. Flexible demand, in the form of flexible domestic electric heating, is shown to have the ability to provide a significant benefit in terms of reduced wind curtailment. This ability is further enhanced for island situations if demand has a frequency-responsive component.The role of distribution networks is changing. There is a significant drive, influenced by climate change and security of supply issues, to move electricity generation towards renewable technologies. This is leading to an increase in demand for renewable generation connections at the distribution network level and putting pressure on distribution network operators to change the 'fit-and-forget' philosophy of network operation to include more active approaches. In the UK this is seen through the development of Active Network Management schemes which manage distributed generation in real-time, applying constraints when required to maintain network limits. In parallel, technologies have been developed that are capable of providing intertemporal flexibility, of which two particular examples are energy storage and flexible demand. The objective of the thesis is to answer the questions: How can energy storage and flexible demand be scheduled in a second-generation Active Network Management scheme? And how should they be operated to gain most benefit from distributed wind generation? To answer these questions, the thesis develops and uses tools to study the optimisation of second-generation Active Network Management schemes including intertemporal technologies. The tools developed include a Dynamic Optimal Power Flow algorithm for management of energy storage and flexible demand. The thesis provides the first fully flexible model of energy storage in this context, the first implementation of principles-of-access in an optimal power flow, and the first detailed study of the role of energy storage and flexible demand in managing thermal limits and reducing curtailment of distributed wind generation. The thesis also develops the theory of Dynamic Locational Marginal Pricing based on the economic information contained in an optimal solution to a Dynamic Optimal Power Flow. The thesis shows this to be a useful way of understanding the economic impact of intertemporal flexibility and monetary flows in markets which contain them. The thesis goes on to provide a detailed report of the application of Dynamic Optimal Power Flow and Dynamic Locational Marginal Pricing to an islanded Active Network Management scheme currently in deployment in the UK. This highlights the ability of the tools developed to contribute to future projects. A conclusions of the thesis is that DOPF provides a useful method of scheduling flexible devices such as energy storage and power systems. It takes full account of network constraints and limitations, and as applied in this thesis, the most complete models of the intertemporal effects of energy storage and flexible demand to date. The studies contained in the thesis show that energy storage and flexible demand can increase the benefit of distributed wind generation in Active Network Management by minimising curtailment and transferring generated electricity to periods during which the energy has greatest value in offsetting expensive, fossil fuel based generation. The thesis notes the importance of a useful definition of the 'benefit' of wind generation in terms of global objectives such as minimising emissions rather than interim objectives such as maximising generation from renewables. The thesis discusses the importance of losses in energy storage, and the relationship of storage and network losses with curtailment of wind and the lost opportunity of generating electricity. In terms of losses, the extension of existing economic analysis methods leads to the result that flexibility will only operate between time-steps where the ratio of prices is greater than the round-trip losses of the store. Within this constraint, effective use of energy storage is shown to result from regular charging and discharging. The comparison between energy storage and flexible demand shows that where there are few losses associated with flexibility in demand it is significantly more successful than energy storage at mitigating the effects of variability in wind. The final study of an islanded distribution network with wind curtailment, concludes that energy storage is less effective that flexible demand at reducing wind curtailment, but can provide benefit through management of peak demand. Flexible demand, in the form of flexible domestic electric heating, is shown to have the ability to provide a significant benefit in terms of reduced wind curtailment. This ability is further enhanced for island situations if demand has a frequency-responsive component