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

Reducing Grid Distortions Utilizing Energy Demand Prediction and Local Storages

© 2021 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.Energy storage systems will play a key role in the establishment of future smart grids. Specifically, the integration of storages into the grid architecture serves several purposes, including the handling of the statistical variation of energy supply through increasing usage of renewable energy sources as well as the optimization of the daily energy usage through load scheduling. This article is focusing on the reduction of the grid distortions using non-linear convex optimization. In detail an analytic storage model is used in combination with a load forecasting technique based on socio-economic information of a community of households. It is shown that the proposed load forecasting technique leads to significantly reduced forecasting errors (relative reductions up-to 14.2%), while the proposed storage optimization based on non-linear convex optimizations leads to 12.9% reductions in terms of peak to average values for ideal storages and 9.9% for storages with consideration of losses respectively. Furthermore, it was shown that the largest improvements can be made when storages are utilized for a community of households with a storage size of 4.6-8.2 kWh per household showing the effectiveness of shared storages as well as load forecasting for a community of households.Peer reviewe

Dimensioning Microgrids for Productive Use of Energy in the Global South—Considering Demand Side Flexibility to Reduce the Cost of Energy

Microgrids using renewable energy sources play an important role in providing universal electricity access in rural areas in the Global South. Current methods of system dimensioning rely on stochastic load profile modeling, which has limitations in microgrids with industrial consumers due to high demand side uncertainties. In this paper, we propose an alternative approach considering demand side management during system design which we implemented using a genetic scheduling algorithm. The developed method is applied to a test case system on Idjwi Island, Democratic Republic of the Congo (DRC), which is to be powered by a micro hydropower plant (MHP) in combination with a photovoltaic (PV) system and a battery energy storage system (BESS). The results show that the increased flexibility of industrial consumers can significantly reduce the cost of electricity. Most importantly, the presented method quantifies the trade-off between electricity cost and consumer flexibility. This gives local stakeholders the ability to make an informed compromise and design an off-grid system that covers their electricity needs in the most cost-efficient way

Hybrid forecast and control chain for operation of flexibility assets in micro-grids

Studies on forecasting and optimal exploitation of renewable resources (especially within microgrids) were already introduced in the past. However, in several research papers, the constraints regarding integration within real applications were relaxed, i.e., this kind of research provides impractical solutions, although they are very complex. In this paper, the computational components (such as photovoltaic and load forecasting, and resource scheduling and optimization) are brought together into a practical implementation, introducing an automated system through a chain of independent services aiming to allow forecasting, optimization, and control. Encountered challenges may provide a valuable indication to make ground with this design, especially in cases for which the trade-off between sophistication and available resources should be rather considered. The research work was conducted to identify the requirements for controlling a set of flexibility assets—namely, electrochemical battery storage system and electric car charging station—for a semicommercial use-case by minimizing the operational energy costs for the microgrid considering static and dynamic parameters of the assets

VPeak: Exploiting Volunteer Energy Resources for Flexible Peak Shaving

Traditionally, utility companies have employed demand response for large loads or deployed centralized energy storage to alleviate the effects of peak demand on the grid. The advent of Internet of Things (IoT) and the proliferation of networked energy devices have opened up new opportunities for coordinated control of smaller residential loads at large scales to achieve similar benefits. In this paper, we present VPeak, an approach that uses residential loads volunteered by their owners for coordinated control by a utility for grid optimizations. Since the use of volunteer resources comes with hard limits on how frequently they can be used by a remote utility, we present machine learning techniques for carefully selecting which days to operate these loads based on expected peak demand. VPeak uses a distributed and heterogeneous pool of volunteer loads to implement flexible peak shaving that can either selectively target hotspots within the distribution network or perform grid-wide peak shaving. Our results show that VPeak is able to shave up to 26% of the total demand when selectively shaving peaks at local hotspots and up to 46.7% of the demand for grid-wide peak shaving

Feasibility Study of Energy Storage Technologies for Remote Microgrid’s Energy Management System

Energy storage systems (ESSs) play a significant role in remote microgrids energy management system (EMS) with the large penetration rate of renewable energy which is intermittent in nature. Energy storage improves system reliability and efficiency in remote microgrids by optimizing the power demand and generation to reduce operational costs. Moreover, it increases the dispatch ability of the energy sources in remote microgrid systems. Lead acid battery (PbA) can be used as an energy storage device in remote microgrids due to its low cost; however, the response rate, short life cycle, and depth of discharge (DoD) lead to high operational costs. Ultracapacitor has a considerably longer life cycle, its energy density is low, and the initial cost is very high. Lithium-ion (Li-ion) and hybrid ion batteries may have comparatively better economical prospects in terms of DoD, life cycle, and operational cost. In this thesis, different energy storage technologies are considered for remote microgrids energy management systems. In addition, the Schiffer weighted Ah throughput model introduces two weight factors to describe that a battery degrades faster in real time operation than the standard test conditions due to different stress factors. These weight factors virtually increase the battery throughput, and accelerate the degradation. To mitigate this problem, different periodical and auto cycling strategies were investigated in this thesis. However, the results demonstrated that frequent full charging prevents the battery from over degradation. Auto cycling strategy was found more cost effective than the periodical cycling. Applying this cycling strategy, the yearly total operational cost of a microgrid system with a 142 kWh PbA battery bank was reduced by 0.62% ($826). Results also showed that the wear cost is an important factor to consider while designing the energy management system. Li-ion and hybrid-ion batteries had lower wear costs and showed great potentiality, although the EMS with a Li-ion battery was found to be 2.55% more cost effective and 1.5% more fuel efficient than hybrid ion batteries. The reduction in operational cost ensures the access to low cost electricity for the people in remote areas. It will accelerate the development of industries, communications, technologies, and the standard of living including the remote health clinics in those areas. Furthermore, the reduction in generators fuel consumption will reduce CO2 emission which will lower the global warming and the greenhouse effect. In this thesis, one of the objectives was to prolong the battery lifetime by preventing the degradation, that may lower the number of yearly battery disposals which are hazardous to the human health and the environment

Application of the Software as a Service Model to the Control of Complex Building Systems

In an effort to create broad access to its optimization software, Lawrence Berkeley National Laboratory (LBNL), in collaboration with the University of California at Davis (UC Davis) and OSISoft, has recently developed a Software as a Service (SaaS) Model for reducing energy costs, cutting peak power demand, and reducing carbon emissions for multipurpose buildings. UC Davis currently collects and stores energy usage data from buildings on its campus. Researchers at LBNL sought to demonstrate that a SaaS application architecture could be built on top of this data system to optimize the scheduling of electricity and heat delivery in the building. The SaaS interface, known as WebOpt, consists of two major parts: a) the investment& planning and b) the operations module, which builds on the investment& planning module. The operational scheduling and load shifting optimization models within the operations module use data from load prediction and electrical grid emissions models to create an optimal operating schedule for the next week, reducing peak electricity consumption while maintaining quality of energy services. LBNL's application also provides facility managers with suggested energy infrastructure investments for achieving their energy cost and emission goals based on historical data collected with OSISoft's system. This paper describes these models as well as the SaaS architecture employed by LBNL researchers to provide asset scheduling services to UC Davis. The peak demand, emissions, and cost implications of the asset operation schedule and investments suggested by this optimization model are analysed