Techno-Economic Analysis and Optimal Control of Battery Storage for Frequency Control Services, Applied to the German Market

Optimal investment in battery energy storage systems, taking into account degradation, sizing and control, is crucial for the deployment of battery storage, of which providing frequency control is one of the major applications. In this paper, we present a holistic, data-driven framework to determine the optimal investment, size and controller of a battery storage system providing frequency control. We optimised the controller towards minimum degradation and electricity costs over its lifetime, while ensuring the delivery of frequency control services compliant with regulatory requirements. We adopted a detailed battery model, considering the dynamics and degradation when exposed to actual frequency data. Further, we used a stochastic optimisation objective while constraining the probability on unavailability to deliver the frequency control service. Through a thorough analysis, we were able to decrease the amount of data needed and thereby decrease the execution time while keeping the approximation error within limits. Using the proposed framework, we performed a techno-economic analysis of a battery providing 1 MW capacity in the German primary frequency control market. Results showed that a battery rated at 1.6 MW, 1.6 MWh has the highest net present value, yet this configuration is only profitable if costs are low enough or in case future frequency control prices do not decline too much. It transpires that calendar ageing drives battery degradation, whereas cycle ageing has less impact.Comment: Submitted to Applied Energ

Learning Agent for a Heat-Pump Thermostat With a Set-Back Strategy Using Model-Free Reinforcement Learning

The conventional control paradigm for a heat pump with a less efficient auxiliary heating element is to keep its temperature set point constant during the day. This constant temperature set point ensures that the heat pump operates in its more efficient heat-pump mode and minimizes the risk of activating the less efficient auxiliary heating element. As an alternative to a constant set-point strategy, this paper proposes a learning agent for a thermostat with a set-back strategy. This set-back strategy relaxes the set-point temperature during convenient moments, e.g. when the occupants are not at home. Finding an optimal set-back strategy requires solving a sequential decision-making process under uncertainty, which presents two challenges. A first challenge is that for most residential buildings a description of the thermal characteristics of the building is unavailable and challenging to obtain. A second challenge is that the relevant information on the state, i.e. the building envelope, cannot be measured by the learning agent. In order to overcome these two challenges, our paper proposes an auto-encoder coupled with a batch reinforcement learning technique. The proposed approach is validated for two building types with different thermal characteristics for heating in the winter and cooling in the summer. The simulation results indicate that the proposed learning agent can reduce the energy consumption by 4-9% during 100 winter days and by 9-11% during 80 summer days compared to the conventional constant set-point strategyComment: Submitted to Energies - MDPI.co

Mussel meal as a potential ingredient in diets for the whiteleg shrimp (Litopenaeus vannamei)

The global aquaculture production is growing immensely in all aspects and has already surpassed the output from wild caught fish and shellfish industries. The farming of Litopenaeus vannamei is one of the biggest contributors to this market. Originally, L. vannamei is native to the tropical marine habitats, but due to its high value, farming of these species expanded to the subtropical areas. Therefore, low temperatures have become one of the major constraining factors to the L. vannamei culture. Besides this, concerns about the sustainability of this industry lead to the search for new, healthy and sustainable ingredients for aquafeeds, like bivalves, due to their nutritional value and low trophic level. In this experiment, mussel meal (species Perna perna) was evaluated as a potential ingredient in L. vannamei diets to improve growth and cold resistance of the shrimp. Five experimental diets (0%, 1%, 2%, 3% and 4% of mussel meal inclusion) were evaluated for 8 weeks in twenty polyethylene tanks of 400 liter (n = 4). Each tank was stocked with 40 shrimps (3.5 ± 0.5 g), filled with sea water and kept under constant aeration and temperature of 28.4 ± 0.4 °C. Every day 100% of the water was exchanged to maintain the water quality. After 8 weeks of experiment a thermal shock treatment was performed to analyse the cold resistance of the shrimp. Shrimps that were fed with the 1% and 2% mussel meal diets had a significantly higher final weight, weekly weight gain and lower FCR than the control, 3% and 4% mussel meal treatments. The shrimps fed with the 2% mussel meal diet had the best growth results. Further, no differences were observed in thermal shock resistance and survival among the treatments. In conclusion, mussel meal can be used as a potential ingredient in whiteleg shrimp diets.A produção global em aquacultura tem tido um crescimento exponencial, tendo já superado o total de peixes e mariscos capturados através da pesca. Um dos segmentos produtivos mais importantes da aquacultura é o grupo dos crustáceos marinhos, com uma contribuição de quase 10% para o mercado global. Dentro deste grupo, a espécie Litopenaeus vannamei, vulgarmente conhecida como camarão de perna branca ou camarão branco do Pacífico, representa 84% da produção total de camarões marinhos produzidos em todo o mundo. Ao considerar todas as espécies produzidas na indústria da aquacultura mundial, L. vannamei foi a 3ª espécie mais produzida em 2018 com um volume total de mais de 6,5 milhões de toneladas, tendo em 2020 apresentado um crescimento na produção de 8,78%, com um valor total de 40 mil milhões de dólares. L. vannamei é uma espécie nativa dos habitats marinhos tropicais, mas devido ao alto valor da produção desta espécie, a sua introdução foi expandida para as áreas subtropicais. Deste modo, as baixas temperaturas tornaram-se um dos principais fatores limitantes na produção de L. vannamei. De forma a manter um crescimento da indústria assente em princípios de sustentabilidade, a aquacultura tem tentado identificar novos ingredientes, saudáveis e sustentáveis do ponto de vista ambiental, tal, como os bivalves. Os bivalves têm um grande potencial como forma de diminuir a pressão sobre a captura de peixes utilizados para a produção de farinha de peixe, pois são ricos em proteínas, lípidos e minerais e minimizam as perdas de energia nas transferências tróficas para a produção de proteína animal, uma vez que são espécies de baixo nível trófico. Nesta experiência, foi testada a utilização da farinha de mexilhão (espécie Perna perna) como ingrediente potencial em dietas de L.vannamei e avaliou-se o seu impacto no crescimento e na resistência térmica dos camarões. No total, cinco dietas experimentais (0%, 1%, 2%, 3% e 4% de inclusão de farinha de mexilhão) foram testadas durante 8 semanas em vinte tanques de polietileno de 400 litros (n = 4). Em cada tanque foram colocados 40 camarões (3,5 ± 0,5 g). Foi utilizado um regime de 12 horas de luz e 12 horas de escuro e cada tanque continha água do mar (30,6 ± 0,4 mg/L) mantida sob aeração constante e a uma temperatura de 28,4 ± 0,4 °C. Todos os dias, 100% da água foi trocada, de forma a manter a sua qualidade. A temperatura e o oxigénio foram medidos duas vezes ao dia (manhã e tarde) usando um medidor YSI Pro 20. Amostras de água foram retiradas semanalmente de cada tanque para medir a concentração de amónia total (TAN) e de nitritos, a alcalinidade, o pH e a salinidade. Os camarões foram alimentados quatro vezes ao dia (8:00, 11:00, 14:00 e 17:00h) com as respetivas dietas, seguindo a tabela de alimentação de Van-Wyk e Scarpa. Uma vez por semana foi determinada a biomassa total de cada tanque e contado o número de camarões. Após as 8 semanas de experiência foi realizado um teste de choque frio potencialmente letal para quantificar a resistência ao frio dos camarões. Dez camarões de cada tanque contendo água do mar a 28,4 ± 0,4°C foram transferidos simultaneamente para aquários de 60 L contendo ± 25 litros de água do mar a 10,9 ± 0,1°C, onde permaneceram por 1 hora, sob aeração constante. Após esse período, foram transferidos para baldes brancos com ± 30 litros com água do mar a 28,5 ± 1,0 °C e a mortalidade foi monitorizada por 24 horas. Após 8 semanas, os camarões que foram alimentados com as dietas com 1% e 2% de farinha de mexilhão tiveram um peso final e um ganho de peso semanal significativamente maior, assim como uma menor taxa de conversão de alimento do que os camarões alimentados com as dietas controlo (com 0% de farinha de mexilhão), 3% e 4% de farinha de mexilhão. Os camarões alimentados com a dieta com 2% de farinha de mexilhão tiveram os melhores resultados de crescimento e atingiram um peso 10% maior que os do tratamento controlo. Além disso, não foram observadas diferenças na resistência ao choque frio e na sobrevivência entre os tratamentos. Com isto, conclui-se que a farinha de mexilhão pode ser utilizada como potencial ingrediente em dietas para camarões.I also want to thank Dr. Philip James, coordinator of the H2020 AquaVitae project (GA 818173), who provided financial support to cover my expenses in Brazil and gave me the opportunity to show my work during the 3rd annual AquaVitae meeting in Porto

PhysQ: A Physics Informed Reinforcement Learning Framework for Building Control

Large-scale integration of intermittent renewable energy sources calls for substantial demand side flexibility. Given that the built environment accounts for approximately 40% of total energy consumption in EU, unlocking its flexibility is a key step in the energy transition process. This paper focuses specifically on energy flexibility in residential buildings, leveraging their intrinsic thermal mass. Building on recent developments in the field of data-driven control, we propose PhysQ. As a physics-informed reinforcement learning framework for building control, PhysQ forms a step in bridging the gap between conventional model-based control and data-intensive control based on reinforcement learning. Through our experiments, we show that the proposed PhysQ framework can learn high quality control policies that outperform a business-as-usual, as well as a rudimentary model predictive controller. Our experiments indicate cost savings of about 9% compared to a business-as-usual controller. Further, we show that PhysQ efficiently leverages prior physics knowledge to learn such policies using fewer training samples than conventional reinforcement learning approaches, making PhysQ a scalable alternative for use in residential buildings. Additionally, the PhysQ control policy utilizes building state representations that are intuitive and based on conventional building models, that leads to better interpretation of the learnt policy over other data-driven controllers.Comment: 15 pages, 4 figures

Transfer Learning in Transformer-Based Demand Forecasting For Home Energy Management System

Increasingly, homeowners opt for photovoltaic (PV) systems and/or battery storage to minimize their energy bills and maximize renewable energy usage. This has spurred the development of advanced control algorithms that maximally achieve those goals. However, a common challenge faced while developing such controllers is the unavailability of accurate forecasts of household power consumption, especially for shorter time resolutions (15 minutes) and in a data-efficient manner. In this paper, we analyze how transfer learning can help by exploiting data from multiple households to improve a single house's load forecasting. Specifically, we train an advanced forecasting model (a temporal fusion transformer) using data from multiple different households, and then finetune this global model on a new household with limited data (i.e. only a few days). The obtained models are used for forecasting power consumption of the household for the next 24 hours~(day-ahead) at a time resolution of 15 minutes, with the intention of using these forecasts in advanced controllers such as Model Predictive Control. We show the benefit of this transfer learning setup versus solely using the individual new household's data, both in terms of (i) forecasting accuracy ($\sim$15\% MAE reduction) and (ii) control performance ($\sim$2\% energy cost reduction), using real-world household data.Comment: 7 pages, 2 figures, workshop article at BALANCES, BuildSys'2