Multiple Energy Carriers Research Articles

Energy Hub Model for the Massive Adoption of Hydrogen in Power Systems

A promising energy carrier and storage solution for integrating renewable energies into the power grid currently being investigated is hydrogen produced via electrolysis. It already serves various purposes, but it might also enable the development of hydrogen-based electricity storage systems made up of electrolyzers, hydrogen storage systems, and generators (fuel cells or engines). The adoption of hydrogen-based technologies is strictly linked to the electrification of end uses and to multicarrier energy grids. This study introduces a generic method to integrate and optimize the sizing and operation phases of hydrogen-based power systems using an energy hub optimization model, which can manage and coordinate multiple energy carriers and equipment. Furthermore, the uncertainty related to renewables and final demands was carefully assessed. A case study on an urban microgrid with high hydrogen demand for mobility demonstrates the method’s applicability, showing how the multi-objective optimization of hydrogen-based power systems can reduce total costs, primary energy demand, and carbon equivalent emissions for both power grids and mobility down to −145%. Furthermore, the adoption of the uncertainty assessment can give additional benefits, allowing a downsizing of the equipment.

Two-stage robust operation of electricity-gas-heat integrated multi-energy microgrids considering heterogeneous uncertainties

With the widespread adoption of combined heat and power and power-to-heat technologies, multi-energy microgrids (MEMGs) have been garnering significant research attention from both industry and academia. However, dealing with uncertainties from renewable energy and load and coordinating multiple energy carriers are the main challenges for MEMG operation. In this regard, a two-stage robust operation method of electricity-gas-heat integrated MEMGs considering heterogeneous uncertainties is proposed in this paper. First, network models for an electricity-gas-heat-based distribution-level MEMG are formulated considering the dynamic characteristics of gas and heat networks. Then, the power-to‑hydrogen-and-heat unit and ladder-type carbon trading mechanism are introduced to reduce the curtailment of wind power and carbon emissions. Further, a two-stage robust optimization (TSRO) method is applied to tackle uncertainties of wind power and load under extreme scenarios in the MEMG operation by iteratively solving the operation problem with the column and constraint generation (C&CG) algorithm. Finally, case studies are conducted to verify our proposed method, demonstrating that it can reduce the multi-energy supply cost while the stepped carbon trading mechanism can also significantly reduce carbon emissions.

Energy Cost Optimization for Incorporating Energy Hubs into a Smart Microgrid with RESs, CHP, and EVs

The energy carrier infrastructure, including both electricity and natural gas sources, has evolved and begun functioning independently over recent years. Nevertheless, recent studies are pivoting toward the exploration of a unified architecture for energy systems that combines Multiple-Energy Carriers into a single network, hence moving away from treating these carriers separately. As an outcome, a new methodology has emerged, integrating electrical, chemical, and heating carriers and centered around the concept of Energy Hubs (EHs). EHs are complex systems that handle the input and output of different energy types, including their conversion and storage. Furthermore, EHs include Combined Heat and Power (CHP) units, which offer greater efficiency and are more environmentally benign than traditional thermal units. Additionally, CHP units provide greater flexibility in the use of natural gas and electricity, thereby offering significant advantages over traditional methods of energy supply. This article introduces a new approach for exploring the steady-state model of EHs and addresses all related optimization issues. These issues encompass the optimal dispatch across multiple carriers, the optimal hub interconnection, and the ideal hub configuration within an energy system. Consequently, this article targets the reduction in the overall system energy costs, while maintaining compliance with all the necessary system constraints. The method is applied in an existing Smart Microgrid (SM) of a typical Greek 17-bus low-voltage distribution network into which EHs are introduced along with Renewable Energy Sources (RESs) and Electric Vehicles (EVs). The SM experiments focus on the optimization of the operational cost using different operational scenarios with distributed generation (DG) and CHP units as well as EVs. A sensitivity analysis is also performed under variations in electricity costs to identify the optimal scenario for handling increased demand.

Anderson Localization of Phonons in Thermally Superinsulating Graphene Aerogels with Metal-Like Electrical Conductivity.

In the quest to improve energy efficiency and design better thermal insulators, various engineering strategies have been extensively investigated to minimize heat transfer through a material. Yet, the suppression of thermal transport in a material remains elusive because heat can be transferred by multiple energy carriers. Here, the realization of Anderson localization of phonons in a random 3D elastic network of graphene is reported. It is shown that thermal conductivity in a cellular graphene aerogel can be drastically reduced to 0.9mWm-1K-1 by the application of compressive strain while keeping a high metal-like electrical conductivity of 120Sm-1 and ampacity of 0.9A. The experiments reveal that the strain can cause phonon localization over a broad compression range. The remaining heat flow in the material is dominated by charge transport. Conversely, electrical conductivity exhibits a gradual increase with increasing compressive strain, opposite to the thermal conductivity. These results imply that strain engineering provides the ability to independently tune charge and heat transport, establishing a new paradigm for controlling phonon and charge conduction in solids. This approach will enable the development of a new type of high-performance insulation solutions and thermally superinsulating materials with metal-like electrical conductivity.

Optimal operation of multiple energy system considering load management approach and uncertainty

In recent years, the advancement of technology has led to the development of energy networks' infrastructure, resulting in the transformation of conventional networks into smart networks. By incorporating communication equipment, each component of the network can now exchange information with other components, creating an environment where innovative ideas can be implemented. One such idea is the utilization of multi-carrier energy systems, also known as Energy Hubs. Integrating energy hubs into traditional networks used to be a challenging task. However, with the expansion and enhancement of smart networks, the potential for incorporating and locating energy hubs within the power grid has significantly increased. In these systems, the focus shifts from optimizing a single energy carrier to ensuring the optimal operation of a system that encompasses multiple energy carriers, such as natural gas, electric grid, and thermal energy. Energy hubs act as intermediaries between energy supply companies and various consumers, including residential, commercial, and industrial sectors. These energy suppliers can encompass different energy networks, such as electricity, gas, and local heating. The integration of residential and commercial energy hubs has become a crucial aspect of modern energy systems, particularly in the context of load response applications. This paper proposes a novel approach for effectively utilizing residential and commercial energy hubs, taking into account the time of use (ToU) load response program. In the proposed system, the inherent uncertainties of the system are addressed using the two-point estimation method of the model. To achieve the desired objective, the Cuckoo optimization algorithm, a well-established meta-heuristic algorithm for solving optimization problems, is employed. This algorithm aids in determining the optimal scheduling of energy resources while considering ToU tariffs and load management plan signals. The results demonstrate that the proposed approach can significantly reduce energy costs for the energy system.

Efficient operation of combined residential and commercial energy hubs incorporating load management and two-point approximation for uncertainty modeling

In the era of advancing technology, energy networks have evolved into sophisticated smart networks, allowing seamless communication and exchange of information among network components. This transformation opens doors for innovative concepts such as the Multi-Carrier Energy System, or Energy Hub. While integrating energy hubs into traditional networks has posed challenges, the expansion of smart networks has facilitated their adoption within the power grid. Unlike conventional systems that optimize a single energy carrier, energy hubs focus on the optimal operation of a system encompassing multiple energy carriers like natural gas, electric grid, and thermal energy. Serving as intermediaries between energy supplier companies and diverse end-users, these hubs play a pivotal role in the intricate landscape of modern energy systems. This paper introduces a novel strategy for optimizing the utilization of residential and commercial energy hubs, specifically integrating Time-of-Use (ToU) tariffs and Load Response Programs (LRPs). To address inherent uncertainties, the proposed system employs the two-point estimation method within its model. For achieving optimal scheduling of energy resources aligned with ToU tariffs and load management plan signals, the well-established Cuckoo Optimization Algorithm (COA) is applied. The results presented in this study highlight the effectiveness of the proposed approach in significantly reducing energy costs for the energy hub. Notably, these cost reductions are achieved while adhering to the constraints of the ToU Load Management Program (LMP). Furthermore, the paper explores various scenarios to investigate the impact of LRPs on both residential and commercial energy hubs, providing valuable insights into the dynamics of these systems. The numerical outcomes of the study affirm the efficacy of the proposed energy hub system. Implementation of load management plans markedly reduces unsupplied electrical and thermal energy, with scenario 3 showcasing a significant drop from 0.52 to 0.2 in unsupplied electrical energy for the home hub.

Optimum exploitation of multiple energy system using IGDT approach and risk aversion strategy and considering compressed air storage with solar energy

The concept of an energy hub within a microgrid has garnered significant attention from investors due to its potential for efficiently managing diverse energy sources and accurately forecasting energy prices. This study presents a model for an energy hub that facilitates the optimal operation of a microgrid with multiple energy carrier infrastructures under future-day scenarios. The primary objective of the optimization process is to minimize operating costs, environmental impacts, and technical limitations. The proposed energy hub model integrates both distributable generation, such as a Combined Cooling, Heating, and Power (CCHP) unit, and non-distributable generation sources, including wind turbines and photovoltaic systems. Additionally, it incorporates various energy storage components, namely Ice Storage Conditioner (ISC), Thermal Energy Storage (TES) systems, and Electric Vehicles (EVs). Furthermore, the study investigates the impact of a novel energy storage system called Solar Powered Compressed Air Energy Storage (SPCAES) on the operational and environmental performance of the energy hub. To address uncertainties stemming from predicted and actual variables, a two-level stochastic optimization problem is formulated based on the Information Gap Decision Theory (IGDT) with a Risk Aversion (RA) strategy. The overarching aim is to mitigate the risks associated with the information gap confronted by microgrid operators. Subsequently, the two-level stochastic optimization problem is transformed into a single-level problem using the Karush-Kuhn-Tucker method. The proposed framework employs the RA-IGDT method and utilizes the Elephant Herd Optimization (EHO) algorithm to effectively solve the optimization problem. By applying this proposed framework to a typical energy hub, this research demonstrates the effectiveness of energy storage systems in reducing operating costs and greenhouse gas emissions during daily energy management. The study highlights the potential of energy hubs and storage systems for cost-effective and eco-friendly microgrid energy management. Scenario 4, featuring the proposed energy hub with SPCAES and ISC storage, demonstrates a 15 % reduction in operating costs and a notable 20 % increase in peak-hour electrical power for sale, underscoring its economic and operational benefits.

Rozwój innowacyjnych hubów energetycznych – panaceum na trudne czasy?

The Energy Hub concept can be defined as a central point where multiple energy carriers meet and the corresponding energy flows can be processed, conditioned, stored and ultimately distributed according to demand requirements in an optimal manner. The energy node modeling concept describes the relationship between input and output energy flows and can be used to optimize energy consumption during planning and operation. The hub can store surplus energy generated by photovoltaics for a long time and cover electricity and heat needs, even during periods of high demand or low renewable production. The research goal of the article is primarily to present the concept of innovation in the field of energy and to identify key energy hubs for the development of the modern economy, as also indicated by one of the authors of the innovation concept, Christensen, who treats distributed energy as the so-called a breakthrough innovation, i.e. one that will gradually replace the classic energy system from the market.

Power market models for the clean energy transition: State of the art and future research needs

As power systems around the world are rapidly evolving to achieve decarbonization objectives, it is crucial that power system planners and operators use appropriate models and tools to analyze and address the associated challenges. This paper provides a detailed overview of the properties of power market models in the context of the clean energy transition. We review common power market model methodologies, their readiness for low- and zero‑carbon grids, and new power market trends. Based on the review, we suggest model improvements and new designs to increase modeling capabilities for future grids. The paper highlights key modeling concepts related to power system flexibility, with a particular focus on hydropower and energy storage, as well as the representation of grid services, price formation, temporal structure, and the importance of uncertainty. We find that a changing resource mix, market restructuring, and growing price uncertainty require more precise modeling techniques to adequately capture the new technology constraints and the dynamics of future power markets. In particular, models must adequately represent resource opportunity costs, multi-horizon flexibility, and energy storage capabilities across the full range of grid services. Moreover, at the system level, it is increasingly important to consider sub-hourly time resolution, enhanced uncertainty representation, and introduce co-optimization for dual market clearing of energy and grid services. Likewise, models should capture interdependencies between multiple energy carriers and demand sectors.

Probabilistic scheduling of a comprehensive energy hub integrated with renewable energy sources considering the correlation between uncertain variables

The energy hub (EH) is one of the customary and efficient energy supplement systems that have been presented, so far. Nowadays, distributed energy resources (DERs), especially renewable energy sources (RESs), are widely integrated into energy systems to supply increasing energy demands. Despite decreasing carbon emissions and other usefulness of employing RESs, uncertainties originating from unpredictable renewable generation besides demand and price uncertainties, confront energy management with new challenges on both the demand and supply sides. The correlation between uncertain parameters intensifies these challenges, severely. In this paper, the optimal operation of a comprehensive EH system including multiple energy carriers is considered in which some inputs are correlated random variables. Uncertainties of the input variables are managed by the k-means clustering technique and correlations among them are handled by the Cholesky decomposition technique. This EH is integrated with compressed air energy storage (CAES) besides other storage facilities. Also, power-to-gas (P2G) as an efficient storage and energy converter is employed in the EH. Other components of the studied EH are combined heat and power (CHP), wind turbine (WT), photovoltaic (PV) cells, boiler, absorption chiller (AC), Electric chiller (EC), fuel-cell power plant (FCPP), and plug-in electric vehicles (PEVs). Considering the undeniable role of demand response programs (DRPs) in reforming load patterns, an aggregated DRP execution besides a sensitivity analysis of load participation factors of DRP is studied in this paper. The proposed optimization problem is formulized as a MILP problem and solved by the GAMS software.

A cost-emission based scheme for residential energy hub management considering comfortable lifestyle and responsible demand

ABSTRACT Due to different and frequently competing goals, managing multiple energy carriers in residential buildings in a coordinated manner is one of the key obstacles to the optimal operation of smart cities. This paper offers a novel conceptual cost-based scheme for optimal energy-gas use in a smart home in the context of residential energy hubs in response to this challenge, considering a significant trade-off between cost reduction and environmental conservation. The suggested model takes into account a variety of energy conversion resources, including energy and heat storage systems, rooftop photovoltaic modules, combined heat and power units, as well as responsible electrical and thermal loads. In addition, an effective stochastic scenario-based approach is used to address the significant uncertainty related to photovoltaic production. By incorporating a weighted summation mixed objective function under various system constraints and user preferences, the suggested framework lowers domestic energy consumption and utility costs while also providing the resident with optimal task scheduling and comfort, which can ensure a high quality of life. The suggested scheme is tested on a real-world case study with energy hubs, and as anticipated, it introduces its applicability and effectiveness in the proposed residential energy hub problem’s optimal energy management. The simulation results show that it is possible to reduce energy procurement costs by up to 47.86% and emissions costs by up to 31.29% while still providing the desired level of comfort for the households.

Influence of high-frequency lake level fluctuations on organic matter accumulation in the northern Qaidam Basin, NW China: Insights from spectral attribute analysis and geochemistry

Assemblages of multiple organic matter-rich sedimentary energy carriers are frequently found in Jurassic nonmarine basins of China. These basins are strongly influenced by high-frequency fluctuations in lake levels. A series of coal- and oil shale-bearing successions reflecting periodic lake level variations is also present in the Shimengou Formation, northern Qaidam Basin, NW China. These Middle Jurassic coal and oil shale seams provide an excellent basis for studying the effects of high-frequency lake level fluctuations on organic matter accumulation. Moreover, additional factors influencing the primary organic matter input (e.g., wildfires) and its preservation and later diagenetic transformation (e.g., processes during coalification) must be considered. In the present study, organic matter maturity, source potential, and paleoenvironmental conditions during source rock formation are investigated based on macro- and micropetrographic data, proximate analyses, bulk geochemical parameters, biomarker analyses, stable isotope geochemistry, and spectral attribute analysis to provide a sequence stratigraphic framework for the accumulation of organic-rich intervals. According to the established depositional model, the Shimengou Formation can be subdivided into four depositional sequences, including three organic matter-rich intervals. These include the thick mixed gas- and oil-prone Sq1 coal seam with a relatively high ash yield, which represents a highstand systems tract, the gas-prone Sq3 coal seam corresponding to a transgressive systems tract, and the Sq4 oil shale layers likely corresponding to a subsequent highstand systems tract, indicating a drowned mire. The results provide insights for the optimization of high-quality sedimentary mineral resource production in similar basins worldwide.

Mid‐Term Bidding Strategy for an Energy Internet Zone Based on Distributionally Robust Optimization

Energy internet zones (EIZ) supply multiple energy carriers to customers in certain regions. They can achieve high energy efficiency by energy cascade utilization. In mid‐term, they participate in the wholesale market and retail market simultaneously. Customers can shift their energy use among different hours and energy carriers to satisfy demand according to the retail prices. Unfortunately, multiple energy systems and markets bring various uncertainties on demand and prices. In this paper, the weekly bidding strategy for the EIZ is proposed to determine the optimal bilateral contracts to sign and the optimal energy retail prices. In retail market, the satisfaction degree and elasticity matrix describe customers' response. Distributionally robust optimization is adopted to consider the distributional uncertainties of random variables and Worst‐case Conditional Value‐at‐Risk is employed for risk management. The distributional uncertainties of pool prices and elasticity matrix are discussed in this paper. An EIZ in Shanghai, China is adopted to illustrate the proposed strategy. The results have great resistance to distributional uncertainties. The risk aversion and satisfaction degree affect the EIZ's strategy in wholesale market and retail market, respectively. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Multi-agent deep reinforcement learning-based optimal energy management for grid-connected multiple energy carrier microgrids

Multi-agent deep reinforcement learning (MA-DRL) method provides a groundbreaking approach to tackling computational problems in power systems, particularly for distributed energy resources that have been widely adopted to advance energy sustainability. This paper presents a novel optimal energy management based on proposed MA-DRL method. This method employs deep neural network to learn strategy based on stacked-denoising auto-encoders and multi-agent deep deterministic policy gradient learning capability. The MA-DRL method is adopted to find the optimal strategy of the optimal energy management problem under the Markov decision process framework. This method aims to coordinate multiple energies and achieve optimal operation over a variety of hourly dispatches while taking into account the distinct properties of electric and thermal energies. The primary challenge of the planning and operation of multiple energy carrier microgrids (MECMs) is determining the optimal interaction between renewable energy resources, energy storage systems, power-to-thermal conversion systems, and upstream power grid in order to improve overall energy utilization efficiency. The presented robust method can adaptively derive the optimal operation for MECMs through centralized learning and decentralized implementation. The optimization problem is employed in this study to concurrently reduce the total emissions and the operating costs while considering engineering design constraints. Finally, to demonstrate the efficiency of the proposed method, it is verified on an integrated modified IEEE 33-bus and 8-node gas systems.

Optimal dispatch of a novel integrated energy system combined with multi-output organic Rankine cycle and hybrid energy storage

Integrated energy system (IES) is characterized as a heterogeneous system, providing energetic, economic and environmental benefits by cooperating with multiple energy carriers. However, current studies rarely focus on utilizing hybrid electric/thermal storage in coordination with harvesting heat flexibly in IES, which should be further considered in the planning period. To this end, a novel IES combined with hybrid electric/thermal storage and multi-output organic Rankine cycle (ORC) is proposed. Considering operating modes of gas turbine, ORC’s working manners, and converting patterns of harvesting heat in IES, comparisons of case study and operating characteristics on typical season days are analyzed. A bi-level optimal planning model of hybrid electric/thermal storage with ORC is proposed, consisting of a lower-level operating model and an upper-level planning model. Results show that there is the least operating cost of IES on typical season days under the off-design mode of gas turbine, ORC’s combined heat and power (CHP) mode and harvesting heat flexibly, when enabling multi-flows to adjust thermoelectric ratio effectively. ORC’s pure power generating mode in IES with low electrical load is not economically attractive, while ORC’s CHP mode is beneficial to enhance ORC’s consistent dispatching time and electricity producing amount. State of charge of battery energy storage system has similar varying patterns, while heat storage degree and cold storage degree of thermal energy storage system change differently owing to varying thermal loads. The IES is thermodynamically feasible and economically attractive thanks to the bi-level optimization, achieving optimal battery energy storage system capacity of 1773 kWh, thermal energy storage system capacity of 4823 kWh and ORC capacity of 91.25 kW.

A Comprehensive Review of the Design and Operation Optimization of Energy Hubs and Their Interaction with the Markets and External Networks

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.

Bidding strategy of integrated energy system considering decision maker’s subjective risk aversion

Improving energy efficient is a promising solution to save energy costs and reduce carbon emission. Based on energy cascade utilization technology, integrated energy systems can supply multiple energy carriers to customers efficiently. However, the integration of multiple energy systems and components brings various uncertainties to integrated energy systems, highlighting the importance of risk management when integrated energy systems participate in energy market. In this paper, spectral risk measure which can model decision makers’ subjective risk aversion is introduced. Two typical measures, exponential spectral risk measure and power spectral risk measure, are discussed and compared. Then, a bidding strategy based on spectral risk measure is proposed for integrated energy systems to participate in short-term energy markets. The optimal self-scheduling and bidding curves in the day-ahead electricity market can be obtained. Compared with the traditional risk measures, the spectral risk measure is a customized risk measure according to decision makers’ subjective attitude and it can improve the decision makers’ subjective preference on the optimal results, as illustrated by case studies.

Applications of Agent-Based Methods in Multi-Energy Systems—A Systematic Literature Review

The need for a greener and more sustainable energy system evokes a need for more extensive energy system transition research. The penetration of distributed energy resources and Internet of Things technologies facilitate energy system transition towards the next generation of energy system concepts. The next generation of energy system concepts include “integrated energy system”, “multi-energy system”, or “smart energy system”. These concepts reveal that future energy systems can integrate multiple energy carriers with autonomous intelligent decision making. There are noticeable trends in using the agent-based method in research of energy systems, including multi-energy system transition simulation with agent-based modeling (ABM) and multi-energy system management with multi-agent system (MAS) modeling. The need for a comprehensive review of the applications of the agent-based method motivates this review article. Thus, this article aims to systematically review the ABM and MAS applications in multi-energy systems with publications from 2007 to the end of 2021. The articles were sorted into MAS and ABM applications based on the details of agent implementations. MAS application papers in building energy systems, district energy systems, and regional energy systems are reviewed with regard to energy carriers, agent control architecture, optimization algorithms, and agent development environments. ABM application papers in behavior simulation and policy-making are reviewed with regard to the agent decision-making details and model objectives. In addition, the potential future research directions in reinforcement learning implementation and agent control synchronization are highlighted. The review shows that the agent-based method has great potential to contribute to energy transition studies with its plug-and-play ability and distributed decision-making process.

Energy optimization of multi-carrier energy systems to achieve a low carbon community

One of the most prominent concerns of human societies, especially in advanced countries, is how to manage and consume energy in a manner that not only energy demand would be supplied but also a minimum amount of pollution enters the environment. In this case, production and consumption resources of energy and carbon dioxides emission sources have to be identified. Optimizing the consumption schedule can reduce fossil fuel consumption and carbon dioxide emission. This project aims to design and plan a multi-energy carrier microgrid that facilitates reaching a low carbon society. In this manner, utilizing modeling techniques of carbon dioxide flow in energy systems offers a good estimation for the amount of carbon distributed in energy systems. This first step is one of the most fundamental aspects of implementing optimization studies with the purpose of carbon reduction. A proper optimization procedure for multiple energy carrier systems at the distribution system level is recommended by proposing suitable and comprehensive decision variables. The optimization studies, which are based on carbon flow in the distribution network, can assist the decision-makers of the energy field in evaluating the effectiveness of various solutions.

Operation and Planning of Energy Hubs Under Uncertainty—A Review of Mathematical Optimization Approaches

Co-designing energy systems across multiple energy carriers is increasingly attracting attention of researchers and policy makers, since it is a prominent means of increasing the overall efficiency of the energy sector. Special attention is attributed to the so-called energy hubs, i.e., clusters of energy communities featuring electricity, gas, heat, hydrogen, and also water generation and consumption facilities. Managing an energy hub entails dealing with multiple sources of uncertainty, such as renewable generation, energy demands, wholesale market prices, etc. Such uncertainties call for sophisticated decision-making techniques, with mathematical optimization being the predominant family of decision-making methods proposed in the literature of recent years. In this paper, we summarize, review, and categorize research studies that have applied mathematical optimization approaches towards making operational and planning decisions for energy hubs. Relevant methods include robust optimization, information gap decision theory, stochastic programming, and chance-constrained optimization. The results of the review indicate the increasing adoption of robust and, more recently, hybrid methods to deal with the multi-dimensional uncertainties of energy hubs.