Optimization of Residential Battery Energy Storage System Scheduling for Cost and Emissions Reductions

The introduction of dynamic electricity pricing structures such as Time of Use (TOU) rates and Day Ahead Pricing (DAP) in residential markets has created the possibility for customers to reduce their electric bills by using energy storage systems for load shifting and/or peak load shaving. While there are numerous system designs and model formulations for minimizing electric bills under these rate structures the use of these systems has the potential to cause an increase in emissions from the electricity system. The Increase in emissions is linked to the difference in fuel mix of marginal generators throughout the day as well as inefficiencies associated with energy storage systems. In this work a multi-objective optimization model is designed to optimize reduction in cost of electricity as well as reduction in carbon dioxide (CO2) emissions from the electricity used by residential customers operating a battery energy storage system under dynamic pricing structures. A total of 22 different regions in the US are analyzed. Excluding emissions from the model resulted in an annual increase of CO2 emissions in all but one region ranging from 60-2000kg per household. The multi-objective model could be used to economically reduce these additional emissions in most regions by anywhere from 5 – 1300kg of CO2 per year depending on the region. When using the multi-objective model several regions had a net decrease in CO2 emissions compared to not using a battery system but most had a net increase

Akkujärjestelmät aurinkosähköjärjestelmän tukena suomalaisissa asuintaloissa

This thesis discusses the use of battery energy storages (BES) with photovoltaic (PV) systems and, in particular, their use in domestic residences in Finland. The main objec-tive is to determine which battery technology is the most promising to add to Naps Solar Systems’ product portfolio for home battery energy storage. The benefits of using the chosen product in parallel with photovoltaic systems is analyzed. The cost savings and other benefits for residential customers are thoroughly analyzed with simulations. The thesis discusses the theory behind photovoltaic cells, and describes their current rate of market penetration. It presents a techno-economic comparison of the three most commonly used battery technologies, i.e. the lead-acid, lithium-ion and nickel batteries. Although the study focuses on battery energy storage for residential, grid-connected customers, it also gives an overview of other energy storage technologies. It examines possible topologies, control systems and the benefits to be gained from battery energy storages, particularly with regard to their profitability. The thesis concludes that battery energy storage based on lithium iron phosphate seems to be the optimal solution for residential PV use. Lithium iron phosphate is one of the safest lithium ion battery technologies, and its cycle lifetime is by far the longest. Therefore, simulations were performed using lithium iron phosphate batteries with capacities ranging from 4-16 kWh. Differently-sized PV systems affect the profitability and operation of a BES system, and these effects are reviewed, as is the impact of having an electric heating system. At the moment, battery energy storage is not a particularly profitable investment. However, most PV-BES systems will reach payback in their lifetime, and they do have a positive net present value. In Finland, the possibility of time-of-use-shifting, coupled with high differences in the spot market price, does indicate some future potential for battery energy storage. At present, these systems are more likely to be bought for ecological rather than economic reasons, and these factors are also considered when recommending the product for Naps Solar Systems

Técnicas de Inteligência Artificial Aplicadas ao Controlo Preditivo de Baterias Estacionárias

A tendência para a descida das chamadas tarifas feed-in que se espera que ocorra nos próximos anos vem ao encontro da necessidade de criar uma rede elétrica mais sustentável, mais autónoma e com maior capacidade de integração de energia vinda de fontes renováveis. Tornar-se-á assim de enorme relevância para os chamados prosumers, consumidores que possuem pequenas unidades de produção distribuída, nomeadamente ao nível doméstico e da pequena indústria, praticarem o chamado auto-consumo. Com os crescentes avanços na tecnologia de baterias estacionários que se refletem acima de tudo na sua viabilidade económica, as baterias estacionárias apresentam-se como uma das melhores soluções, a par dos veículos elétricos, para maximizar os níveis de auto-consumo dos prosumers.Os controladores que são hoje utilizados na gestão das ações de carga e descarga destas baterias têm, contudo, uma atuação reativa e imediata. Tornar-se-ia interessante para um prosumer que estes controladores tivessem uma ação que por um lado fosse preditiva, isto é, capaz de perceber de que forma irão evoluir os consumos e a produção para maximizar os níveis de auto-consumo. Se, por outro lado, considerarmos que o prosumer se encontra contratualizado num regime de mercado, o controlador deverá ter também uma atuação oportunista, jogando com os preços do mercado para que a requisição de energia à rede fosse feita, sempre que possível, em horas onde o preço fosse mais barato.Este problema enquadra-se matematicamente nas definições multi-objetivo e multi-temporal. Associando-lhe o elevado número de variáveis de estado que, no caso de serem previsões, virão afetas de erros torna-o de tal ordem complexo que apenas pode ser endereçado por agentes de inteligência artificial.No presente trabalho é avaliada a capacidade de técnicas de inteligência artificial no controlo preditivo de baterias estacionárias acopladas a unidades de produção fotovoltaica. Nomeadamente é avaliado o método de Proximal Policy Gradient disponibilizado pela OpenAI, inserido na categoria das metodologias de Deep Reinforcement Learning, que combinam redes neuronais com o treino de agentes artificiais através de Reinfocement Learning. É efetuada a sua comparação com algoritmos genéticos de modo a inferir a viabilidade desta metodologia na resolução do problema em questão.The expected trend of decreasing feed-in tariffs in the upcoming years meets the current necessity to secure a more sustainable and autonomous electric power grid, capable of integrating more renewable energy resources. This trend turns self-consumption particularly relevant for prosumers (consumers that own small distributed generation units), namely at the household and small industry levels. With the growing advances in stationary storage technologies, reflected utmost at their economic viability, stationary batteries along with electric vehicles are viewed as one of the best solutions to maximize such self-consumption levels of prosumers.Today's storage controllers, used on the management of charging and discharging these batteries present a reactive and immediate response. It can although be more interesting, for a prosumer, that such controllers could present a more predictive action, i.e., capable of understanding how consumption and production profiles will evolve, in order to maximize the self-consumption. If we also consider the prosumer to be involved in a market dynamic pricing scheme, the controller should also behave opportunistically, taking into account the market prices so that the energy requirements made to grid would be deviated to time windows were prices were cheaper.This problem can be mathematically framed on the definitions of multi-objective and multi-temporal. Associating the elevated number of state variables and the error possibilities inherent to the data's forecasting nature makes this problem extremely complex, narrowing its resolution to techniques based on artificial intelligence.In the present work, the capability of artificial intelligence techniques in predictively controlling stationary storage when coupled with photovoltaic generation units, is evaluated. Namely it is used the Proximal Policy Gradient method, made available by OpenAI and inserted in the category of Deep Reinforcement Learning which combine neural networks with the training of artificial intelligence agents through Reinforcement Learning. The comparison with genetic algorithms is made in order to infer the viability of this methodology in the resolutions of the problem at hand