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Advancing Multi-Energy Hub Design: an Integrated Approach for Optimizing Residential Clusters in High RES Penetration Scenarios
Abstract
With the energy transition underway, the wider penetration of multi-energy hubs (MEHs) is inevitable as they allow for the integration of multiple energy carriers at the local level and especially at the users' side by enhancing flexibility of energy supply and renewabIes penetration. Thus, addressing their design properly is imperative. While numerous energy modeling software and tools are extensively discussed in the literature, they primarily focus on a single energy carrier, mainly dedicated to the electrical network. In this study, an optimization planning tool capable of analyzing the synergies of various energy carriers is presented. The tool aims to determine the optimal configuration of MEHs with sizes of the chosen technologies based on the modeler's preferences. The tool offers flexibility in modelling a wide range of technologies as potential options for the optimal configuration, and in considering different types of objectives as economic and environmental ones that can be assessed through a multi-objective approach. It is formulated through mixed-integer linear programming in a modular manner, facilitating the easy implementation of new emerging technologies, by enhancing scalability and applicability in real context. To prove the effectiveness of the optimization tool, it is applied for the design of a MEH for a residential building cluster located in Torino (Italy). Different scenarios are analyzed to determine the impact of high levels of renewabIes penetration on the design of the MEH while guaranteeing the economic sustainability of the solution.
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The European Union’s vision for energy transition not only foresees decarbonization of the electricity sector, but also requires commitment across different sectors such as gas, heating, and cooling through an integrated approach. It also sets local energy communities at the center of the energy transition as a bottom-up approach to achieve these ambitious decarbonization goals. The energy hub is seen as a promising conceptual model to foster the optimization of multi-carrier energy systems and cross-sectoral interaction. Especially in the context of local energy communities, the energy hub concept can enable the optimal design, management, and control of future integrated and digitalized networks where multiple energy carriers operate seamlessly and in complementarity with each other. In that sense, the optimal design and operation of energy hubs are of critical importance, especially under the effect of multiple objectives taking on board not only technical, but also other aspects that would enable the sustainability of local energy communities, such as economic and environmental. This paper aims to provide an in-depth review of the literature surrounding the existing state-of-the-art approaches that are related to the design and operation optimization of energy hubs by also exploring their interaction with the external network and multiple markets. As the planning and operation of an energy hub is a multifaceted research topic, this paper covers issues such as the different optimization methods, optimization problems formulation including objective functions and constraints, and the hubs’ optimal market participation, including flexibility mechanisms. By systematizing the existing literature, this paper highlights any limitations of the approaches so far and identifies the need for further research and enhancement of the existing approaches.
Aiming at integrating different energy sectors and exploiting the synergies coming from the interaction of different energy carriers, sector coupling allows for a greater flexibility of the energy system, by increasing renewables' penetration and reducing carbon emissions. At the local level, sector coupling fits well in the concept of an integrated local energy community (ILEC), where active consumers make common choices for satisfying their energy needs through the optimal management of a set of multi-carrier energy technologies, by achieving better economic and environmental benefits compared to the business-as-usual scenario. This paper discusses the stochastic operation optimization of the smart Savona Campus of the University of Genoa, according to economic and environmental criteria. The campus is treated as an ILEC with two electrically interconnected multi-energy hubs involving technologies such as PV, solar thermal, combined heat and power systems, electric and geothermal heat pumps, absorption chillers, electric and thermal storage. Under this prism, the ILEC can participate in the day-ahead market (DAM) with proper bidding strategies. To assess the renewables' uncertainties, the roulette wheel method is used to generate an initial set of scenarios for solar irradiance, and the fast forward selection algorithm is then applied to preserve the most representative scenarios, while reducing the computational load of the next optimization phase. A stochastic optimization model is thus formulated through mixed-integer linear programming (MILP), with the aim to optimize the operation strategies of the various technologies in the ILEC, as well as the bidding strategies of the ILECs in the DAM, considering both energy costs and carbon emissions through a multi-objective approach. Case study results show how the optimal bidding strategies of the ILEC on the DAM allow minimizing of the users' net daily cost, and, as in the case of environmental optimization, the ILEC operates in self-consumption mode. Moreover, in comparison to the current operation strategies, the optimized case allows reduction of the daily net energy cost in a range from 5 to 14%, and the net daily carbon emissions in a range from 6 to 18%.
The study of future energy scenarios with high shares of variable renewable energy sources (VRES) require an accurate representation of VRES variability and storage capacity. However, long-term optimal expansion models, which are typically used to prescribe the evolution of energy systems, make use of coarse time series to limit computational effort. This weakness can entail an incorrect sizing of VRES plants and storage facilities. In this work, a novel method is proposed to mitigate the current limitations and enable accurate long-term planning of high-VRES decarbonisation pathways. Clustering methods are applied to time series, preserving the possibility of having inter-day and intra-day energy storage. To this end, the temporal framework of an open-source energy system model, OSeMOSYS, is modified to allow the implementation of interconnected, clustered representative days. Traditional and novel approaches are compared and benchmarked for a reference case study, i.e., a remote island. The results show that time series clustering can significantly improve the evaluation of the overall system cost, leading to a relative error of -5% (novel approach) instead of -35% (traditional approach) when 24 representative days are considered. Similarly, the new approach improves the sizing of VRES and storage facilities. The new technique is found to require three orders of magnitude less computation time than the traditional technique to achieve a comparable level of accuracy.
About 75% of the world's energy consumption takes place in cities. Although their large energy consumption attracts a large number of research projects, only a small fraction of them deal with approaches to model energy systems of city districts. These are particularly complex due to the existence of multiple energy sectors (multi-energy systems, MES), different consumption sectors (mixed-use), and different stakeholders who have many different interests.
This contribution is a review of the characteristics of energy system models and existing modeling tools. It evaluates current studies and identifies typical characteristics of models designed to optimize MES in mixed-use districts. These models operate at a temporal resolution of at least 1 h, follow either bottom-up or hybrid analytical approaches and make use of mixed-integer programming, linear or dynamic.
These characteristics were then used to analyze minimum requirements for existing modeling tools. Thirteen of 145 tools included in the study turned out to be suitable for optimizing MES in mixed-use districts. Other tools where either created for other fields of application (12), do not include any methodology of optimization (39), are not suitable to cover city districts as a geographical domain (44), do not include enough energy or demand sectors (20), or operate at a too coarse temporal resolution (17). If additional requirements are imposed, e.g. the applicability of non-financial assessment criteria and open source availability, only two tools remain.
Overall it can be stated that there are very few modeling tools suitable for the optimization of MES in mixed-use districts.
This paper presents a thorough review of 75 modelling tools currently used for analysing energy and electricity systems. Increased activity within model development in recent years has led to several new models and modelling capabilities, partly motivated by the need to better represent the integration of variable renewables. The purpose of this paper is to give an updated overview of currently available modelling tools, their capabilities and to serve as an aid for modellers in their process of identifying and choosing an appropriate model. A broad spectrum of modelling tools, ranging from small-scale power system analysis tools to global long-term energy models, has been assessed. Key information regarding the general logic, spatiotemporal resolution as well as the technological and economic features of the models is presented in three comprehensive tables. This information has been validated and updated by model developers or affiliated contact persons, and is state-of-the-art as of the submission date. With the available suite of modelling tools, most challenges of today's electricity system can be assessed. For a future with an increasing share of variable renewables and increasing electrification of the energy system, there are some challenges such as how to represent short-term variability in long-term studies, incorporate the effect of climate change and ensure openness and transparency in modelling studies.
One of the main ways to reach the important targets of the European Commission in the energy field set by 2030 is to implement sustainable energy systems to cover electrical and thermal energy demands, as well as transportation needs, in urban areas. In the aforesaid scenario, multi-vector energy hubs consist of installing small and medium size power plants, mainly fed by renewable sources, and charging infrastructures for electric vehicles in sustainable urban districts. The optimal design of such systems can be faced from different points of view and using different methods, one of the most adopted being the development of mathematical programming algorithms typically aimed to minimize global costs and emissions. The contribution of this paper is to propose an innovative methodology to optimally design a multi-vector energy hub able to supply electricity and thermal energy, for space heating and cooling, to a set of buildings within a sustainable district acting as a local energy community. The developed mixed integer linear programming model is characterized by very detailed models of the technologies and by a multi-objective function which is proposed to evaluate the impact of the optimal solution on global costs and emissions. The proposed mathematical model is very comprehensive as it involves a wide range of energy technologies with detailed modelling by considering the impact of external ambient conditions and energy vector temperature values on the plant conversion efficiency, thus showing high replicability potential in real contexts. In the case study, the model is applied to a university campus in the North of Italy.
The European Union defined the ambitious carbon-neutrality goal for 2050. Such a transition must be done gradually to avoid huge investments; therefore, medium-term energy planning of energy supply systems must be performed properly considering both economic and environmental aspects. Sector coupling measures aid to achieve this ambitious target, although they require a remarkable financial investment. This paper presents an innovative methodology for the medium-term energy planning of the university campus “Marche Polytechnic University” located in Italy towards the carbon neutrality, i.e., 50 % of carbon emissions reduction by considering financial investment aspects. The university campus is a multi-carrier local energy community with multiple technologies such as photovoltaic, combined heat and power, gas-fired boilers, absorption, and electric chillers that satisfy the end-users’ energy demand. A different mix of installed and new technologies (e.g., energy storage or hydrogen) are investigated through the Calliope framework. The case studies present the economic-based optimal scenario of a typical year planning, guaranteeing the same 50 % carbon emissions reduction. Results underline the importance of exploiting synergies among multiple carriers and the essential role of i) renewables (e.g., additional 3.3 MW of photovoltaic to be installed), ii) batteries with a capacity of 7 MWh, and (iii) sector coupling technologies.
In this paper, a multi-objective Energy Management System (EMS) for polygeneration microgrids is presented. The proposed tool has been developed within the LIVING GRID project, funded by the Italian Ministry of Research (actions related to the Italian Technology Cluster on Energy), and it is characterized by a detailed representation of generation units and flexible loads, as well as electric/thermal networks and storage systems that can be present in microgrids and sustainable districts. A new optimization model has been developed in which the objective function is related to the minimization of costs, the maximization of the overall exergy efficiency of the system, and the minimization of stress indexes. To solve efficiently the optimization problem, a novel algorithm based on an accelerated version of the Bregman Alternating Direction Method of Multipliers (with Heavy Ball dual acceleration term) has been developed and discussed. The decision model has been applied to a real case study represented by the Savona Campus of the University of Genova in Italy. A sensitivity analysis has been performed for evaluating the effect of each term of the objective function on the solution.
Although distributed energy resources (DER) are growing fast, to achieve their expected benefits in the context of local energy systems, it is essential to determine the system configurations in a rational way by selecting the most appropriate energy technologies and the corresponding operation strategies, while taking into account both cost and environmental sustainability aspects. This chapter comprehensively discusses the issue of optimal planning of local integrated energy systems with multiple energy carriers through a multi-objective approach in order to find cost-sustainability trade-off solutions. The optimization models proposed are adaptable and scalable, and thus applicable in contexts going from nanogrids to energy communities. The multi-objective approach proposed allows identifying the best possible trade-off solutions between economic and sustainability objectives on the Pareto frontier, thus providing various design options for planners according to short- and long-run priorities. In the chapter, mathematical models of a wide range of energy technologies, optimization models, and related solution methodologies are presented. Moreover, in order to demonstrate the effectiveness of the proposed models, several case studies are discussed by involving different scales from a multi-energy nanogrid associated with a single-family house to an integrated energy community with several types of end users.
Energy system models are crucial to plan energy transition pathways and understand their impacts. A vast range of energy system modelling tools is available, providing modelling practitioners, planners, and decision-makers with multiple alternatives to represent the energy system according to different technical and methodological considerations. To better understand this landscape, here we identify current trends in the field of energy system modelling. First, we survey previous review studies, identifying their distinct focus areas and review methodologies. Second, we gather information about 54 energy system modelling tools directly from model developers and users. Unlike previous questionnaire-based studies solely focusing on technical descriptions, we include application aspects of the modelling tools, such as perceived policy-relevance, user accessibility, and model linkages. We find that, to assess the possible applications and to build a common understanding of the capabilities of these modelling tools, it is necessary to engage in dialogue with developers and users. We identify three main trends of increasing modelling of cross-sectoral synergies, growing focus on open access, and improved temporal detail to deal with planning future scenarios with high levels of variable renewable energy sources. However, key challenges remain in terms of representing high resolution energy demand in all sectors, understanding how tools are coupled together, openness and accessibility, and the level of engagement between tool developers and policy/decision-makers.
The present evolution of the energy systems is characterized by an increase of small-size solutions, with which new aggregations of local integrated energy systems are emerging. The current policies on cleaner and efficient energy systems are providing opportunities to the development of systems supplied by renewable energy sources, and to the combined production from multiple energy carriers. Regulatory evolutions are enabling the existence and operation of local energy markets and energy communities.
This chapter provides an overview of the relevant literature concerning the use of distributed energy resources in the context of local energy systems. The chapter is organized in three parts. In the first part, the aspects addressed include the deployment of local solutions to generate, store, and manage energy at small scale and micro-scale. The second part deals with grid-related aspects and solutions for optimal grid operation. The third part discusses various emergent aspects referring to local energy systems, markets and energy communities, which are gaining relevance in the present and future context of energy transition.
In this paper, a multi-objective optimization model is proposed to obtain the optimized configuration of interconnected distributed energy resource (DER) systems in a local energy community (LEC), while considering economic and environmental aspects. The objective is the optimal selection and sizing of DER with corresponding operation strategies, and the optimal configuration of the heating pipeline network, which allows the heat exchange among the DER systems. The economic objective is to minimize the total annual cost, whereas the environmental objective is to minimize the total annual CO2 emissions. The Pareto frontier is found through the weighted-sum method, by using branch-and-cut. Numerical results show that the design method allows identifying different configurations of the interconnected DER systems and heating pipeline network on the Pareto frontier, thereby providing trade-off options to planners for economic/environmental sustainability of the LEC. Moreover, the total annual cost and emissions of the LEC with the optimized configurations are significantly reduced as compared with the conventional energy supply scenario.