Peak Forecasting for Battery-based Energy Optimizations in Campus Microgrids

Battery-based energy storage has emerged as an enabling technology for a variety of grid energy optimizations, such as peak shaving and cost arbitrage. A key component of battery-driven peak shaving optimizations is peak forecasting, which predicts the hours of the day that see the greatest demand. While there has been significant prior work on load forecasting, we argue that the problem of predicting periods where the demand peaks for individual consumers or micro-grids is more challenging than forecasting load at a grid scale. We propose a new model for peak forecasting, based on deep learning, that predicts the k hours of each day with the highest and lowest demand. We evaluate our approach using a two year trace from a real micro-grid of 156 buildings and show that it outperforms the state of the art load forecasting techniques adapted for peak predictions by 11-32%. When used for battery-based peak shaving, our model yields annual savings of $496,320 for a 4 MWhr battery for this micro-grid.Comment: 5 pages. 4 figures, This paper will appear in the Proceedings of ACM International Conference on Future Energy Systems (e-Energy'20), June 202

Engineering Top-Down Weight-Balanced Trees

Weight-balanced trees are a popular form of self-balancing binary search trees. Their popularity is due to desirable guarantees, for example regarding the required work to balance annotated trees. While usual weight-balanced trees perform their balancing operations in a bottom-up fashion after a modification to the tree is completed, there exists a top-down variant which performs these balancing operations during descend. This variant has so far received only little attention. We provide an in-depth analysis and engineering of these top-down weight-balanced trees, demonstrating their superior performance. We also gaining insights into how the balancing parameters necessary for a weight-balanced tree should be chosen - with the surprising observation that it is often beneficial to choose parameters which are not feasible in the sense of the correctness proofs for the rebalancing algorithm.Comment: Accepted for publication at ALENEX 202

Zipping Segment Trees

Stabbing queries in sets of intervals are usually answered using segment trees. A dynamic variant of segment trees has been presented by van Kreveld and Overmars, which uses red-black trees to do rebalancing operations. This paper presents zipping segment trees - dynamic segment trees based on zip trees, which were recently introduced by Tarjan et al. To facilitate zipping segment trees, we show how to uphold certain segment tree properties during the operations of a zip tree. We present an in-depth experimental evaluation and comparison of dynamic segment trees based on red-black trees, weight-balanced trees and several variants of the novel zipping segment trees. Our results indicate that zipping segment trees perform better than rotation-based alternatives

Multi-objective particle swarm optimization for optimal scheduling of household microgrids

Addressing the challenge of household loads and the concentrated power consumption of electric vehicles during periods of low electricity prices is critical to mitigate impacts on the utility grid. In this study, we propose a multi-objective particle swarm algorithm-based optimal scheduling method for household microgrids. A household microgrid optimization model is formulated, taking into account time-sharing tariffs and users’ travel patterns with electric vehicles. The model focuses on optimizing daily household electricity costs and minimizing grid-side energy supply variances. Specifically, the mathematical model incorporates the actual input and output power of each distributed energy source within the microgrid as optimization variables. Furthermore, it integrates an analysis of capacity variations for energy storage batteries and electric vehicle batteries. Through arithmetic simulation within the Pareto optimal solution set, the model identifies the optimal solution that effectively mitigates fluctuations in energy input and output on the utility side. Simulation results confirm the effectiveness of this strategy in reducing daily household electricity costs. The proposed optimization approach not only improves the overall quality of electricity consumption but also demonstrates its economic and practical feasibility, highlighting its potential for broader application and impact

Feasibility, and Resiliency, and Economic Impacts of Energy Storage in Urban Water Systems: Case Study of Cleveland, Tennessee

This research reduces the knowledge gap around how the water-energy nexus can be applied at the urban level, clarifying how a city water distribution system might be used to offset community energy consumption. The created methodology includes 3 research objectives. First, model scenarios are developed to determine opportunities for energy storage in urban water systems. Then, how increased energy storage capacity impacts water system resiliency is examined. Finally, the financial implications of scenarios are calculated.A closed-loop water system model (EPANET2) simulates Cleveland, Tennessee’s water distribution system, resolved to the neighborhood scale. Sectoral aggregated hourly energy use data provides a comparison baseline for storage scenarios. Storage is injected into the water model in concentrated and distributed configurations to understand which is more effective at shaving peak energy demands, and which is more effective at increasing water system resiliency. Configurations are assigned costs, to understand how feasible it is to increase energy storage in water systems over local utility planning and financing horizons.Key findings include: (1) concentrated water storage configurations can generate significantly more electricity than distributed storage configurations, because they can be designed primarily for energy generation, not primarily to meet demand and to maintain pressure; (2) distributed water storage configurations can be more resilient to the chronic stress of population growth, because increasing storage throughout the water system is more effective at maintaining water system pressures and meeting increasing demand; and (3) neither concentrated nor distributed water storage configurations are cost effective within local utility planning and financing horizons, because the payback periods far exceed that 20-year timeframe.This research fills a knowledge gap around the scale at which small pumped hydro-generation systems can be effective at reducing community electrical demands. It clarifies the impacts of various storage configurations on water system resiliency, and how fiscally solvent using the water system to store energy might be. It concludes that small-scale hydro in an urban water system is viable at the micro-scale on a case by case basis, but not fiscally feasible as a tool to shave peak community energy demands

Balancing of intermittent renewable generation in smart grid

This thesis researches a novel electricity demand response method and renewable energy management technique. It demonstrated the use of flow batteries and residential hot water heaters to balance wind power deviation from plan. The electricity supply-demand balancing problem becomes increasingly more difficult. A large portion of complexity to this problem comes from the fact that most renewable energy sources are inherently hard to control and intermittent. The increasing amount of renewable energy generation makes scientists research new supply-demand balancing possibilities to adapt for the changes. In this research wind power data was used in most cases to represent the supply side. The focus is on the actual generation deviation from plan, i.e. forecasting error. On the other hand, the methods developed in this thesis are not limited to wind power balancing. Two major approaches were analysed - heating ventilation and air conditioning system control (mainly focused on, but not limited to, residential hot water heaters) and hybrid power system comprising of thermal and hydro power plants together with utility scale flow batteries. These represent the consumption side or the demand response mechanism. The first approach focused on modelling the behaviour of residential end users. Artificial intelligence and machine learning techniques such as neural networks and Box-Jenkins methodology were used to learn and predict energy usage. Both joint and individual dwelling behaviour was considered. Model predictive control techniques were then used to send the exact real-time price and observe the change in electricity consumption. Also, novel individual hot water heater controllers were modelled with the ability to forecast and look ahead the required energy, while responding to electricity grid imbalance. It proved to be possible to balance the generation and increase system efficiency while maintaining user satisfaction. For the second approach, the hybrid multi-power plant system was exploited. Three different power sources were modelled, namely thermal power plant, hydro power pant and flow battery. These sources were ranked by the ability to rapidly change the output of electricity. The power that needs to be balanced was then routed to different power units according to their response times. The calculation of the best power dispatch is proposed using a cost function. The aim of this research was to accommodate for the wind power imbalance without sacrificing the health of the power plants (minimising load variations for sensitive units)

Microgrids: Planning, Protection and Control

This Special Issue will include papers related to the planning, protection, and control of smart grids and microgrids, and their applications in the industry, transportation, water, waste, and urban and residential infrastructures. Authors are encouraged to present their latest research; reviews on topics including methods, approaches, systems, and technology; and interfaces to other domains such as big data, cybersecurity, human–machine, sustainability, and smart cities. The planning side of microgrids might include technology selection, scheduling, interconnected microgrids, and their integration with regional energy infrastructures. The protection side of microgrids might include topics related to protection strategies, risk management, protection technologies, abnormal scenario assessments, equipment and system protection layers, fault diagnosis, validation and verification, and intelligent safety systems. The control side of smart grids and microgrids might include control strategies, intelligent control algorithms and systems, control architectures, technologies, embedded systems, monitoring, and deployment and implementation

Optimization approaches for exploiting the load flexibility of electric heating devices in smart grids

Energy systems all over the world are undergoing a fundamental transition to tackle climate change and other environmental challenges. The share of electricity generated by renewable energy sources has been steadily increasing. In order to cope with the intermittent nature of renewable energy sources, like photovoltaic systems and wind turbines, the electrical demand has to be adjusted to their power generation. To this end, flexible electrical loads are necessary. Moreover, optimization approaches and advanced information and communication technology can help to transform the traditional electricity grid into a smart grid. To shift the electricity consumption in time, electric heating devices, such as heat pumps or electric water heaters, provide significant flexibility. In order to exploit this flexibility, optimization approaches for controlling flexible devices are essential. Most studies in the literature use centralized optimization or uncoordinated decentralized optimization. Centralized optimization has crucial drawbacks regarding computational complexity, privacy, and robustness, but uncoordinated decentralized optimization leads to suboptimal results. In this thesis, coordinated decentralized and hybrid optimization approaches with low computational requirements are developed for exploiting the flexibility of electric heating devices. An essential feature of all developed methods is that they preserve the privacy of the residents. This cumulative thesis comprises four papers that introduce different types of optimization approaches. In Paper A, rule-based heuristic control algorithms for modulating electric heating devices are developed that minimize the heating costs of a residential area. Moreover, control algorithms for minimizing surplus energy that otherwise could be curtailed are introduced. They increase the self-consumption rate of locally generated electricity from photovoltaics. The heuristic control algorithms use a privacy-preserving control and communication architecture that combines centralized and decentralized control approaches. Compared to a conventional control strategy, the results of simulations show cost reductions of between 4.1% and 13.3% and reductions of between 38.3% and 52.6% regarding the surplus energy. Paper B introduces two novel coordinating decentralized optimization approaches for scheduling-based optimization. A comparison with different decentralized optimization approaches from the literature shows that the developed methods, on average, lead to 10% less surplus energy. Further, an optimization procedure is defined that generates a diverse solution pool for the problem of maximizing the self-consumption rate of locally generated renewable energy. This solution pool is needed for the coordination mechanisms of several decentralized optimization approaches. Combining the decentralized optimization approaches with the defined procedure to generate diverse solution pools, on average, leads to 100 kWh (16.5%) less surplus energy per day for a simulated residential area with 90 buildings. In Paper C, another decentralized optimization approach that aims to minimize surplus energy and reduce the peak load in a local grid is developed. Moreover, two methods that distribute a central wind power profile to the different buildings of a residential area are introduced. Compared to the approaches from the literature, the novel decentralized optimization approach leads to improvements of between 0.8% and 13.3% regarding the surplus energy and the peak load. Paper D introduces uncertainty handling control algorithms for modulating electricheating devices. The algorithms can help centralized and decentralized scheduling-based optimization approaches to react to erroneous predictions of demand and generation. The analysis shows that the developed methods avoid violations of the residents\u27 comfort limits and increase the self-consumption rate of electricity generated by photovoltaic systems. All introduced optimization approaches yield a good trade-off between runtime and the quality of the results. Further, they respect the privacy of residents, lead to better utilization of renewable energy, and stabilize the grid. Hence, the developed optimization approaches can help future energy systems to cope with the high share of intermittent renewable energy sources