Search Results (5)

The advancement in battery manufacturing has played a significant role in the use of batteries as a cost-effective energy storage system. This paper proposes an optimal charging and discharging strategy for the battery energy storage system deployed for economic dispatch and supply/demand balancing services in the presence of intermittent renewables such as solar photovoltaic systems. A decision-making strategy for battery charge/discharge operations in a discrete-time rolling horizon framework is developed as a finite-input set non-linear model predictive control instances and a dynamic programming procedure is proposed for its real-time implementation. The proposed scheme is tested on controllable loads and a photovoltaic generation scenario in the premises of a sports centre, as a part of a pilot demonstration of the inteGRIDy EU-funded project. The test results confirm that the implemented stacking of the battery and optimal decision-making algorithm can enhance net saving in the electricity bill of the sports centre, and lead to corresponding CO₂ reductions. Full article

Cost–benefit analysis is a common evaluation method applied to assess whether an energy system is economically feasible as well as the economic viability of energy investment for the energy transition of a pre-existing energy system. This paper focuses on examining the economic costs and benefits obtained through the implementation of renewable energy and smart technology to a pre-existing energy system of two pilot sites—St. Jean and Barcelona. The evaluation process includes all relevant parameters such as investment, operating and maintenance costs, and energy prices needed to assess the economic feasibility of the investment. The results show that investing in energy system development towards a decarbonized future, can provide various benefits such as increased flexibility, and reduced emissions while being economically feasible. Full article

This paper presents an assessment of the impacts of the different tools implemented within the inteGRIDy project through the analysis of key performance indicators (KPIs) that appropriately reflect the technical and economic domains of the inteGRIDy thematic pillars, comprising demand response and battery storage systems. The evaluation is based on improvements brought about by individual components of the inteGRIDy-enabled smart solution across the Isle of Wight (IOW) pilot site. The analyses and the interpretation of findings for the pilot use case evaluation are presented. The results indicate that the smart solution implementation across the IOW pilot site resulted in achieving the inteGRIDy set objectives. Overall, a 93% reduction in energy consumption, equivalent to 643 kWh was achieved, via the M7 energy storage system and heat pumps developed as part of inteGRIDy solution. Additionally, the grid efficiency and demand flexibility contribution to the distribution network operator (DNO)-triggered DR services, based on a 10% increase/decrease in demand, resulted in stabilizing the grid efficiency. Full article

Advances in metering technologies and emerging energy forecast strategies provide opportunities and challenges for predicting both short and long-term building energy usage. Machine learning is an important energy prediction technique, and is significantly gaining research attention. The use of different machine learning techniques based on a rolling-horizon framework can help to reduce the prediction error over time. Due to the significant increases in error beyond short-term energy forecasts, most reported energy forecasts based on statistical and machine learning techniques are within the range of one week. The aim of this study was to investigate how facility managers can improve the accuracy of their building’s long-term energy forecasts. This paper presents an extensive study of machine learning and data processing techniques and how they can more accurately predict within different forecast ranges. The Clarendon building of Teesside University was selected as a case study to demonstrate the prediction of overall energy usage with different machine learning techniques such as polynomial regression (PR), support vector regression (SVR) and artificial neural networks (ANNs). This study further examined how preprocessing training data for prediction models can impact the overall accuracy, such as via segmenting the training data by building modes (active and dormant), or by days of the week (weekdays and weekends). The results presented in this paper illustrate a significant reduction in the mean absolute percentage error (MAPE) for segmented building (weekday and weekend) energy usage prediction when compared to unsegmented monthly predictions. A reduction in MAPE of 5.27%, 11.45%, and 12.03% was achieved with PR, SVR and ANN, respectively. Full article

Many new demand response strategies are emerging for energy management in smart grids. Real-Time Energy Pricing (RTP) is one important aspect of consumer Demand Side Management (DSM), which encourages consumers to participate in load scheduling. This can help reduce peak demand and improve power system efficiency. The use of Intelligent Decision Support Systems (IDSSs) for load scheduling has become necessary in order to enable consumers to respond to the changing economic value of energy across different hours of the day. The type of scheduling problem encountered by a consumer IDSS is typically NP-hard, which warrants the search for good heuristics with efficient computational performance and ease of implementation. This paper presents an extensive evaluation of a heuristic scheduling algorithm for use in a consumer IDSS. A generic cost model for hourly pricing is utilized, which can be configured for traditional on/off peak pricing, RTP, Time of Use Pricing (TOUP), Two-Tier Pricing (2TP) and combinations thereof. The heuristic greedily schedules controllable appliances to minimize smart appliance energy costs and has a polynomial worst-case computation time. Extensive computational experiments demonstrate the effectiveness of the algorithm and the obtained results indicate the gaps between the optimal achievable costs are negligible. Full article