• Energy Research
• 11. Sustainability close

With the new Dutch regulation on banning the newly-built petrol vehicles by 2030, urban areas are predicted in the short-term local congestion situation dues to the high penetration level of electric vehicles. These vehicles with fast charging technologies require a high electric demand over some hours to charge their batteries. In order to provide a feasible solution at the utility level to that problem, large-scale battery energy storage systems (BESS) are considered as vital means to provide flexibility and effectively support the local networks. However, the capital cost of the BESS is still a main concern leading to a cost-effective operation schedule is required to ensure the desirable power quality while the lifetime of BESS is extended. This paper investigates the solutions to the power congestion problem and under-voltage issue of a Dutch medium voltage network around the Amsterdam Arena stadium once the penetration of electric vehicles keeps increasing in the years to come. The coordination scheme among BESS is then proposed to ensure the maximum life span of BESS. The numerical results from this work confirm the feasibility and effectiveness of the large-scale BESS in modern distribution networks and suggest the potential benefit that the stadium owner could gain when their BESS system supports the local network.

The increasing penetration of distributed energy resources (DERs) in the distribution grid, in combination with the liberalization of the electricity markets, introduces small-scale electricity producers in a market architecture designed for large-scale power plants. This work examines a double-auction electricity market for a residential area, designed to facilitate the electricity exchange between local consumers and prosumers. The motivation for implementing such a market is twofold. First, to integrate DERs in the electricity market architecture. Second, to ensure the economic viability of renewable energy generators in the absence of subsidized support schemes. In this work, a local market structure with photovoltaic (PV) generators and dispatchable fuel-based generators is considered as a use-case to examine the efficiency of the local electricity market. The efficiency of the market is determined by comparing the average market price of the double auction to the competitive price of the market operating ideally.

In the smart grid and smart city context, the energy end-user plays an active role in the operation of the power system. The rapid penetration of Renewable Energy Sources (RES) and Distributed Energy Resources (DER) requires a higher degree of flexibility on the demand side. As commercial and Industrial buildings (C&I) buildings represent a substantial aggregation of loads, the intertwined operation of the electric distribution network and the built environment is to large extent responsible for achieving energy efficiency and sustainability targets. However, the primary purpose of buildings is not grid support but rather ensuring the comfort and safety of its occupants. Therefore, the comfort level needs to be included as a constraint when assessing the flexibility potential of the built environment. This paper proposes a decentralized method for flexibility allocation among a set of buildings. The method uses concepts from non-cooperative game theory. Finally, two case of study are used to evaluate the performance of the decentralized algorithm, and compare it against a centralized option. It is shown that flexibility requests from the grid operator can be met without deteriorating the comfort levels.

Energy flexibility is becoming more significant part of future power systems. Buildings can be a massive source of flexibility to face the challenges associated with the Smart Grid. Specially, a collection of buildings or a neighborhood can offer better flexibility by managing their energy profile. There are a number of standard frameworks developed to provide support for the design of different smart grid use cases and to facilitate the interoperations among different actors in the smart grid context. But there is a lack of a comprehensive architecture framework which specifies the role of smart neighborhood. This paper reviews the existing research frameworks of Smart Grids and how to incorporate future intelligent neighborhood energy management systems into these frameworks. The analysis identifies gaps in the existing frameworks and proposes a modular interoperability framework focusing on the smart neighborhood aspect.

New energy technologies like photovoltaics, electric vehicles, and heat pumps increasingly find their way to distribution networks. At the time the existing distribution networks were designed, only conventional loads were considered. The capacity of the (existing) distribution networks is therefore insufficient to handle the additional (bidirectional) peak load caused by these new technologies. The distribution system operator (DSO) is facing network congestion. Flexibility to shift and/or change power and energy in time and/or amount is considered as an option to mitigate network congestion with various implicit and explicit mechanisms. This leaves DSOs with the question on how to deploy such mechanisms seamlessly and effectively in daily business with uncertain congestion scenarios and complex integration processes. To tackle these operational challenges, this paper introduces a generic four-step approach to operationalize the flexibility need of a DSO, for any chosen implementation of an implicit or explicit flexibility mechanism (e.g. price-based schemes, flexibility markets, direct control). To this end, this paper addresses the steps: 1. Data acquisition, 2. Forecasting, 3. Decision-making, and 4. Flexibility mechanism interfacing. Furthermore, a particular implementation is described in relation to the Dutch’ H2020 InterFlex demonstrator, showing the field application of the proposed steps. In this demonstrator, a large amount of flexibility (26 electric vehicle charge points of 22kW, a 250kW/315kWh battery energy storage system, and a 260kWp photovoltaic installation) is connected to two 630kVA transformers in a residential area with approximately 350 apartments. The results of the implementation show that the proposed steps enable the DSO to predict congestion, put a monetary value on flexibility, and use this value to evaluate flexibility offered through – in this case study – a flexibility market.

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).