• Energy Research

Abstract The deployment of innovative energy management (EM) approaches based on systematic modelling and optimisation techniques has received an increasing amount of attention in the last decade. This has been often prompted by more stringent energy policy objectives aiming at reducing carbon emissions, phasing out nuclear plants and promoting overall energy efficiency, while containing both capital and operating costs. In this respect the energy hub concept has proven to be a popular approach for operating technologies and units comprising diversified energy carriers, small-scale production units, storage devices and converter systems. Additionally, developments in communication network and control infrastructure afford the possibility, at least in principle, to actively steer and adjust the load on the demand side of the energy balance, leading to the formulation of demand side management (DSM) techniques. This paper proposes an EM solution that combines the features and advantages of both of the aforementioned approaches, i.e. the energy hub framework and DSM methods. The key idea is to combine the supply-side characteristics of energy hubs with the demand side flexibility yielded by the deployment of DSM schemes. This combined approach is validated on an existing building complex by formalizing its energy supply system as an integrated hub and by modelling its heating demand based on thermodynamic principles. Numerical results based on this experimental setup are presented, illustrating that the combined approach can lead to overall savings typically exceeding 10% compared to a baseline scenario where no EM solution is applied, i.e. where only a rule-based heuristic is employed to control the available energy assets, and underscoring the advantages brought by a systematically integrated modelling and optimisation approach. The proposed solution is thus of interest for a broad host of installations in the residential and commercial domain, and for the latter a specific real-world example has been explicitly considered and analysed. The obtained results are encouraging and warrant further analysis and investigation.

Solving the issue of energy security for geographical islands presents a one-of-a-kind problem that has to be tackled from multiple sides and requires an interdisciplinary approach that transcends just technical and social aspects. With many islands suffering in terms of limited and costly energy supply due to their remote location, providing a self-sustainable energy system is of utmost importance for these communities. In order to improve upon the status quo, novel solutions and projects aimed at increasing sustainability not only have to consider optimal utilization of renewable energy potentials in accordance with local conditions, but also must include active community participation. This paper analyzes both of these aspects for island communities and brings them together in an optimization scenario that is utilized to determine the relationship between supposed demand flexibility levels and achievable savings in a setting with variable renewable generation. The results, specifically discussed for a use case with real-world data for the La Graciosa island in Spain, show that boosting community participation and thus unlocking crucial demand flexibility, can be used as a powerful tool to augment novel generation technologies with savings from flexibility at around 7.5% of what is achieved purely by renewable sources.

The paper proposes an innovative software system to support advanced microgrid planning and operation optimization services in the context of complex, multi-carrier, energy systems with diverse legacy equipment. The proposed approach focus on retrofitting existing supervision and control systems, employed for information and data collection from site as well as application of devised control actions, with advanced ICT technologies, such as semantic web, to develop context aware framework for advanced energy management. Application of the proposed solution in a real world use-case achieved operational cost savings exceeding 10%.

Through the use of effectively unlimited energy from the environment, contemporary heat pump installations have proven to be a highly efficient means for satisfying both heating and cooling needs. As such, they are often regarded as a key driver in the electrification process of the thermal domain. Keeping in mind the significant share of the thermal demand in building energy requirements, this aspect in particular is projected to have a great impact on the overall energy efficiency improvement of buildings and increase of demand-side flexibility. The latter point is considered crucial for improving the precarious balance against the contemporary intermittent power supply obtained from a variety of renewable sources. However, in the process of increasing their efficiency, heat pump systems are becoming significantly more complex with combinations of various different sources of energy from the ground, air and the Sun. By extension, the complexity of managing their operation has also increased and proves to be a challenge even for experienced plant operators. In order to aid in this regard, relevant literature suggests that optimization algorithms can be considered as a viable tool. This paper explores the limited extent of existing state-of-the-art approaches for hybrid heat pump control via metaheuristic optimization and broadens the subject matter with a comparative study that analyzes and benchmarks several metaheuristic approaches found in the literature on similar problems. A real-world multisource heat pump installation in Ferrara is utilized as the basis for the study.

For each photovoltaic power plant, it is extremely important to perform an analysis of its efficiency, as well as an analysis of all parameters that may affect efficiency. The electric energy of the photovoltaic system, which is delivered to the electric power system on a daily basis, is determined through the average daily insolation, the surface of the panel and the average efficiency value. One of the parameters that affect the conversion efficiency of a photovoltaic power plant is a decrease in the conversion efficiency due to an increase in panel temperature. In this paper an example is a real photovoltaic power plant with a nominal power of 50 kW, which is installed on the rooftop of the building of the Institute "Mihajlo Pupin", located in Zvezdara forest, Belgrade, Serbia. The correlation analysis of the estimated temperature of the photovoltaic panel was performed using two models and the measured temperature of the photovoltaic panel. The temperature of the photovoltaic panel was estimated using models, one of which does not take into account, and the other takes into account the influence of wind speed on the temperature of the panel.

Energy hub modelling and analysis has garnered a significant degree of attention in the last few years, with different approaches and formalisms being proposed to provide a unified framework for dealing with such systems. The present work is derived from one such approach and presents a numerical analysis of a real world case-study based on the Bilbao Exhibition Centre. The modelling and optimisation method is employed to assess the economic profitability of upgrading the system by expanding its portfolio of energy assets and evaluating the impact over a number of years of operation.

Abstract The paper presents a methodology that has been developed and implemented for the simulation of electricity use (kWh) as well as electricity pricing (c€/kWh) in buildings. In the simulation, electricity pricing has been treated as dependent on electricity use as energy pricing tariffs usually discriminate between specific electricity consumption zones, time periods, etc. Pricing and electricity use, together, may then provide an estimation of the building electric energy cost (€). The simulation runs on an hourly resolution allowing to highlight some potential wasteful and costly practices in energy use and management and providing insight to user behavior, which has been a key driver for its development. The key aspects of the simulation approach are presented and its use is demonstrated in two different buildings in Greece and Italy. The methodology is now adapted to allow for neural network based, real time training, which will also be briefly introduced.

Apart from numerous technical challenges, the transition towards a carbon-neutral energy supply is greatly hindered by limited economic feasibility of renewable energy sources. This results in their slow and bounded penetration in both commercial and residential sectors that are responsible for over 40% of final energy consumption. This paper aims to demonstrate that combined application of sophisticated planning methodologies at building-level and presents incentive mechanisms for renewables that can result in prosumers, featuring hybrid renewable energy systems (HRES), with economic performance comparable to that of conventional energy systems. The presented research enhances existing planning methodologies by integrating appliance-level demand side management into the decision process and investigates its effect on the planning problem. Moreover, the proposed methodology features an innovative and holistic approach that simultaneously assess electrical and thermal domain in both an isolated and grid-connected context. The analyzed hybrid system consists of solar photovoltaic, wind turbine and battery with thermal supply featuring solar thermal collector and a ground-source heat pump. Overall optimization problem is modeled as a mixed-integer linear program, while ranking of all feasible alternatives is made by the multicriteria decision-making algorithm against several technological, economic, and environmental criteria. A real-life scenario of energy system retrofit for a building in the United Kingdom was employed to demonstrate overall cost savings of 12% in the present market and regulation context.