• Energy Research
• 13. Climate action close

The increasing penetration of Renewable Energy Sources (RES), the liberalization of the electricity markets across the world and devices such as smart meters to realize bidirectional communication with centralized computer systems present the end-users of the power system with new opportunities to decrease their electricity costs or become active electricity market participants. However, the intermittent nature of RES and dynamic electricity prices require tools against uncertainty to protect the end-users from under utilizing their assets. In this work, we examine the effectiveness of Robust Optimization (RO)in maximizing the economic benefit of the owner of a home battery storage system in the presence of uncertainty in dynamic electricity prices. The advantage of the robust model is that it keeps its linear class, thus it is not too computationally intensive to be included in the control algorithm of a residential energy storage controller. We examine the effectiveness of the RO model in doing dynamic electricity price arbitrage and in providing fast response to requests from an aggregating entity for power injection or absorption. In the use-case, the aggregating entity makes requests for flexibility and coordinates 100 such devices using a price signal. The results indicated that the RO approach can be beneficial for a non-conservative agent in the case of low daily price fluctuations. However, if the daily price fluctuations are higher, as in summer, the agent is better off by ignoring uncertainty in the dynamic electricity prices.

To improve the design of the electricity infrastructure and the efficient deployment of distributed and renewable energy sources, a new paradigm for the energy supply chain is emerging, leading to the development of smart grids. There is a need to add intelligence at all levels in the grid, acting over various time horizons. Predicting the behavior of the energy system is crucial to mitigate potential uncertainties. An accurate energy prediction at the customer level will reflect directly in efficiency improvements in the whole system. However, prediction of building energy consumption is complex due to many influencing factors, such as climate, performance of thermal systems, and occupancy patterns. Therefore, current state-of-the-art methods are not able to confine the uncertainty at the building level due to the many fluctuations in influencing variables. As an evolution of artificial neural network (ANN)-based prediction methods, deep learning techniques are expected to increase the prediction accuracy by allowing higher levels of abstraction. In this paper, we investigate two newly developed stochastic models for time series prediction of energy consumption, namely Conditional Restricted Boltzmann Machine (CRBM) and Factored Conditional Restricted Boltzmann Machine (FCRBM). The assessment is made on a benchmark dataset consisting of almost four years of one minute resolution electric power consumption data collected from an individual residential customer. The results show that for the energy prediction problem solved here, FCRBM outperforms ANN, Support Vector Machine (SVM), Recurrent Neural Networks (RNN) and CRBM.

To realize the goals of energy transition, becoming energy-neutral at the neighborhood level by sharing energy among clusters of heterogeneous buildings with local distributed energy resources (DERs), will play a vital role. However, uncertainties related to demand and renewable sources pose a major operational challenge to schedule the DERs. In this paper, a scenario-based mixed-integer linear programming (MILP) model is proposed for an energy management system (EMS) of a local energy community. The proposed EMS executes a stochastic day-ahead scheduling operation of multi-energy systems (MES). A set of scenarios are generated with the Gaussian mixture model (GMM) to consider uncertainties of demand and renewable sources. Moreover, Monte Carlo simulations (MCS) are performed to assess the effectiveness of the proposed EMS compared to the deterministic one. The proposed method is validated by using a real-world case study of a generic Dutch university medical campus in Amsterdam, the Netherlands. Two types of analysis are performed: one-day analysis and seasonal analysis. In both cases, in an average, the stochastic process outperforms the deterministic process considerably, in terms of cost, CO2 emission, imported electricity from grid and usage of local energy resources.

Within the smart cities concept, smart buildings and smart grids have emerged as crucial solutions to increase energy efficiency, without jeopardizing the main objectives of such systems. As the power system go through the transition from a "centralized" and "vertical" structure, to a "decentralized" and "horizontal" one, its different building blocks need to guarantee energy efficiency and sustainability. In the built environment, Building Energy Management Systems (BEMS) offer promising flexibility for Demand Side Management and Demand Responses (DSM&DR), while interacting with smart grids. Through the use of intelligent control techniques, BEMS can be optimized to exploit the built environment's demand flexibility in the smart grid context while ensuring the minimum comfort levels required by the buildings users. This paper proposes an agent-based control strategy for the operation of buildings. A fuzzy rule based decision making strategy to monitor and control the energy flows in a building is implemented. Through the exchange of relevant information, the operation of the building can be dynamically influenced to improve energy efficiency.

The increasing number of decentralized renewable energy sources together with the grow in overall electricity consumption introduce many new challenges related to dimensioning of grid assets and supply-demand balancing. Approximately 40% of the total energy consumption is used to cover the needs of commercial and office buildings. To improve the design of the energy infrastructure and the efficient deployment of resources, new paradigms have to be thought up. Such new paradigms need automated methods to dynamically predict the energy consumption in buildings. At the same time these methods should be easily expandable to higher levels of aggregation such as neighbourhoods and the power distribution grid. Predicting energy consumption for a building is complex due to many influencing factors, such as weather conditions, performance and settings of heating and cooling systems, and the number of people present. In this paper, we investigate a newly developed stochastic model for time series prediction of energy consumption, namely the Conditional Restricted Boltzmann Machine (CRBM), and evaluate its performance in the context of building automation systems. The assessment is made on a real dataset consisting of 7 weeks of hourly resolution electricity consumption collected from a Dutch office building. The results showed that for the energy prediction problem solved here, CRBMs outperform Artificial Neural Networks (ANNs), and Hidden Markov Models (HMMs).

Low energy buildings (LEBs) are being considered as a promising solution for the built environment to satisfy high-energy efficiency standards. The technology is based on lowering the overall heat transmission coefficient value (U-value) of the buildings envelope and increasing a heat capacity thus creating a higher thermal inertia. However, LEB introduces a large time constant compared to conventional building due to which it slows the rate of heat transfer between interior of building and outdoor environment and alters the indoor climate regardless of sudden changes in climatic conditions. Therefore, it is challenging to estimate and predict thermal energy demand for such LEBs. This work focuses on artificial intelligence (AI) model to predict energy consumption of LEB. Two kinds of AI modeling approaches: “all data” and “relevant data” are considered. The “all data” uses all available training data and “relevant data” uses a small representative day dataset and addresses the complexity of building non-linear dynamics by introducing past day climatic impacts behavior. This extraction is based on dynamic time warping pattern recognition methods. The case study consists of a French residential LEB. The numerical results showed that “relevant data” modeling approach that relies on small representative data selection has higher accuracy (R2 = 0.98; RMSE = 3.4) than “all data” modeling approach (R2 = 0.93; RMSE = 7.1) to predict heating energy load.