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This study focuses on harnessing excess heat from Power-to-X technologies in future energy hubs. By coupling thermodynamics with an optimization model, a novel analysis is performed using the Greenlab Skive energy hub, which includes hydrogen and methanol synthesizers, as a case study. Pinch analysis techniques are employed to design a heat exchanger network that can thermally integrate processes within the energy hub. Through the EnerHub2X modelling framework, three scenarios are optimized to explore and compare the potential of excess heat recovery. Excess heat is utilized through an optimally designed heat exchanger network, which achieves up to 3.49 MW of heat recovery. This leads to increased profits due to reduced natural gas costs and higher methanol sales. Upgrading excess heat and distributing it to the district heating network yields the highest profit, driven by increased compressed hydrogen and methanol production and the sale of heat to district heating. Thermal integration reduces waste heat by 14.5%, while utilizing excess heat in the district heating network results in 60% further reduction. An economic assessment confirms the feasibility and profitability of the project. Sensitivity analyses are undertaken to analyse the impact of critical parameters on the model, such as electricity and heat prices, along with the selling prices of methanol and hydrogen. The results highlight that excess heat utilization enhances profitability, energy efficiency, and sustainability. By implementing waste heat recovery techniques and exploring alternative options, future energy hubs can strengthen their business models and play a crucial role in Europe's green transition and clean fuel production. • Pinch analysis is combined with a multi-carrier energy system model. • Excess heat utilization value from Power-to-X production in energy hubs is assessed. • Effective use of excess heat significantly reduces waste heat and gas consumption. • Connection of large-scale electrolysis to district heating provides additional value. [ABSTRACT FROM AUTHOR]

Transactive Energy Control (TEC) as a market-based control is a critical notion for scheduling Multi-Carrier Energy Systems (MCESs) in local networks and forming an Energy Hub (EH). Nevertheless, implementing TEC for scheduling and controlling MCESs is extremely difficult due to the lack of a cooperative TEC model that accounts for network constraints and the uncertainty of Renewable Energy Sources (RESs). This paper defines and formulates Prosumer-Based Multi-Carrier Energy Systems (PB-MCESs), which include electricity, heat, cooling, and gas hubs to enable internal coordination of resources and flexibility extraction for PB-MCESs. Subsequently, Nash Bargaining Game Theory is employed to construct a cooperative TEC that prioritizes P2P energy trade. In addition to P2P energy trading, PB-MCESs can trade their reserve in a P2P fashion to mitigate their uncertainty. PB-MCESs estimate the level of uncertainty using stochastic programming and allot a reserve capacity based on this estimation in order to manage their uncertainty via P2P reserve trading and internal reserves. PB-MCES can also control their risk by altering their risk-taking factor in accordance with the Conditional Value-at-Risk (CVAR) index. Implementations have demonstrated that the proposed cooperative TEC decreases total costs by 17.14% and that the proposed P2P reserve trading reduces total costs by 16.32%. [Display omitted] • Prosumer-based multi-carrier energy modeling. • A fair P2P energy + reserve trading. • Nash bargaining game-based market. • Convex system constraints modeling. • Risk assessment using CVAR. [ABSTRACT FROM AUTHOR]

Greenhouses are implemented all over the world to increase agricultural production thanks to controlled environmental conditions (inside temperature, moisture and CO 2 contents). However, such systems are energy-intensive. The presented work focuses on controlling a greenhouse' onsite multi-energy system (gas, heat and electricity), extended to a multi-flow system as the CO 2 produced by the energy units is used as a plant fertilizers. In this view, a three levels hierarchical control has been developed: a steady state economic MPC is combined with a dynamic Multi-energy MPC and low-level PID controllers. This new controller aims at determining the best synergies for economic flows management, subject to compliance with the required climatic conditions in the greenhouse. The proposed controller is applied to a greenhouse which energy system is based on a thermal energy storage fueled by a gas boiler and a combined heat and power unit. The results are confronted to a usual ruled-based controller performed by greenhouses owners, showing a more efficient dynamic functioning of the energy system. Consequently, less gas is consumed (80.79 t to 76.03 t), heat is produced when needed, and electricity from the CHP is economically optimized allowing less importation from the grid (from 17.80 MWh to 15.88 MWh), and profitable selling. • Greenhouses are multi-flow systems combining heat, electricity, gas and CO 2. • Hierarchical control is proposed as an alternative to the usual rule-based controller. • The second level of the controller uses an artificial neural network as dynamic model. • The controller allows efficient dynamic operation and energy and economical savings. [ABSTRACT FROM AUTHOR]

This paper proposes a systematic approach to evaluate the load carrying capability of a district multi-energy system (MES) using the Energy Hub (EH) modeling approach. The EH approach is used to model the system, and the EH steady-state security region concept is introduced as a means of assessing the system's load carrying capacity. To calculate the security region, a multi-parametric programming (MPP) algorithm is proposed, while a standardized matrix modeling approach is utilized to formulate its mathematical representation. To handle the high-dimensional polytope resulting from time coupling, a strategy is employed to approximate the original polytope using a reduced number of lower-dimensional polytopes. These lower-dimensional polytopes are specifically associated with a limited number of time periods, thereby alleviating the computational complexity. The proposed method offers an improved balance between computational accuracy and efficiency compared to existing approaches, while retaining the inherent time coupling characteristics across different period combinations. • Proposed MPP method efficiently calculates security region of energy hubs. • Fast approximation method designed to handle computational burden. • Time coupling has significant impact on security region size and feasibility. • Proposed method outperforms traditional methods in terms of accuracy and efficiency. • Results demonstrate the importance of considering time coupling in security region analysis. [ABSTRACT FROM AUTHOR]

• Study interdependencies of energy demand and supply side in coupled optimization. • Development of co-simulation and optimization framework, combining simulators. • New bi-objective formulation for simultaneous building and energy hub optimization. • Comparison of coupled optimization to regular, consecutive approach. • Showing importance of simultaneous design of buildings and multi-energy systems. This paper presents a co-simulation framework for the simultaneous optimization of building geometries and multi-energy systems using the energy hub approach. The rationale for such coupling is that building geometry has an impact on energy demands and solar potentials on roofs and façades, thus also altering the corresponding optimal energy system. As a demonstration of this approach, we formulate bi-objective optimization problems for minimizing operational cost and carbon emissions, with decision variables for building geometry and for selection and sizing of energy system technologies. The methodology is applied to a case study in the city of Zurich, Switzerland, involving four office buildings. Different carbon emissions targets are studied to show the impact on cost, densities, sizing of the multi-energy system, and architectural design implications. Results for this case study show a clear relation between emissions targets and densities due to available solar potentials for renewable energy generation, with an optimal density in the strictest emissions target reaching only 10% of the optimal density without emissions target. This indicates that differentiated environmental targets should be defined depending on the location of a building, where rural sites could have stricter targets than dense urban sites to reflect the respective marginal costs for achieving the targets. Our study shows that coupling multiple simulators into a common optimization and design workflow brings together architectural aspects, such as geometry, with engineering aspects, such as the energy system design, and microclimate conditions, such as local solar potentials. Thus, essential interdependencies between the energy supply and demand side can be captured in the design of energy efficient cities. [ABSTRACT FROM AUTHOR]

Highlights • A hierarchical modeling for integrated energy management system is set up. • Information masking mechanism is proposed to address the information security. • Modeling of energy hub and load aggregators is considered in the optimization model. Abstract Under the development of information and communication technologies, this paper discusses a cloud-based user-side integrated energy management to improve the energy utilization efficiency in a smart community, for which a two-level model is proposed that relies on an energy hub and load aggregators. Furthermore, to address the issue of preserving confidentiality for the cloud service, an information-masking mechanism is designed based on linear mapping functions, whose information-masking requirements and processes are specified accordingly. Numerical results demonstrate the effectiveness of the proposed model and of the method for cloud computing. [ABSTRACT FROM AUTHOR]

Recently, waste heat recovery has enabled data centers to serve as energy prosumers with flexible features that play essential roles in the coordination operation of integrated electricity–heat systems (IEHSs). The energy hub, as a critical component of IEHSs, provides a promising opportunity to improve energy efficiency. In this paper, the decentralized coordination optimization for IEHSs with data centers is explored to protect the confidential information of different entities. First, a novel data center based energy hub (DCEH) model is developed, where energy conversion, consumption and storage present high flexibility. Especially, in addition to heat recovery, several other controllable operational characteristics of a single data center are considered, involving servers, workloads and indoor temperature. Then, the coordination optimization model of IEHSs with DCEH (IEHSs-DCEH) is constructed by incorporating energy consumption cost and carbon emission, where the energy networks of IEHSs are considered. Moreover, a learning-aided relaxed alternating direction method of multipliers (LR-ADMM) algorithm is proposed to solve the dispatching model of IEHSs-DCEH considering communication packet loss. The proposed LR-ADMM algorithm embeds a momentum extrapolation based prediction technique, which can obtain the predicted value of missing boundary information without adding computational burden, even in continuous packet losses. Simulation results demonstrate that the developed coordination dispatching model can achieve higher economic and environmental benefits than the reference one that ignores data centers' flexibility. Additionally, the proposed LR-ADMM algorithm with proper prediction factors exhibits a faster convergence rate and robustness hedging against packet losses compared to the ADMM and relaxed ADMM approaches. • A highly flexible data center based energy hub model is developed. • Impacts of the flexibility in the data center based energy hub are investigated. • The coordination of integrated energy systems with data centers is optimized. • A learning-aided decentralized algorithm is proposed hedging against packet loss. [ABSTRACT FROM AUTHOR]

Highlights • It describes the motivation of a cluster approach and defines its associated boundary dimensions. • A set of influencing factors on urban energy systems at cluster level are identified. • Three main categories of RES envelope solutions are summarized. • Modelling techniques for energy systems at cluster level are reviewed. Abstract The emergence of renewable-energy-source (RES) envelope solutions, building retrofit requirements and advanced energy technologies brought about challenges to the existing paradigm of urban energy systems. It is envisioned that the building cluster approach—that can maximize the synergies of RES harvesting, building performance, and distributed energy management—will deliver the breakthrough to these challenges. Thus, this paper aims to critically review urban energy systems at the cluster level that incorporate building integrated RES solutions. We begin with defining cluster approach and the associated boundaries. Several factors influencing energy planning at cluster scale are identified, while the most important ones are discussed in detail. The closely reviewed factors include RES envelope solutions, solar energy potential, density of buildings, energy demand, integrated cluster-scale energy systems and energy hub. The examined categories of RES envelope solutions are (i) the solar power, (ii) the solar thermal and (iii) the energy-efficient ones, out of which solar energy is the most prevalent RES. As a result, methods assessing the solar energy potentials of building envelopes are reviewed in detail. Building density and the associated energy use are also identified as key factors since they affect the type and the energy harvesting potentials of RES envelopes. Modelling techniques for building energy demand at cluster level and their coupling with complex integrated energy systems or an energy hub are reviewed in a comprehensive way. In addition, the paper discusses control and operational methods as well as related optimization algorithms for the energy hub concept. Based on the findings of the review, we put forward a matrix of recommendations for cluster-level energy system simulations aiming to maximize the direct and indirect benefits of RES envelope solutions. By reviewing key factors and modelling approaches for characterizing RES-envelope-solutions-based urban energy systems at cluster level, this paper hopes to foster the transition towards more sustainable urban energy systems. [ABSTRACT FROM AUTHOR]

Highlights • Three organization schemes are compared and aggregation is the most efficient. • Sharing scheme can achieve near optimal efficiency without a central organizer. • Concrete bilevel models for energy hub management in multi-energy system. • An approximated mixed-integer linear program for computing the market equilibrium. Abstract The interdependency across natural gas, power and heating systems is increasingly tightened due to the wide development of cogeneration plants and electrified heating facilities. Multi-energy integration is a prevalent trend and the energy hub, which acts as an intermediary agent between providers and consumers, is expected to play a central role in allocating energy resources more efficiently. However, uncertainties originating from multiple kinds of energy demands challenge the operation of energy hubs and may compromise system efficiency. Energy trading and sharing among individual hubs offer a unique opportunity to increase system flexibility and reduce the cost under demand uncertainty. In this paper, three quintessential schemes for organizing a cluster of energy hubs at demand side, i.e., individual, sharing market, and aggregation, are studied under a stochastic framework with probabilistic load forecasts. First, we perform theoretical analysis and compare their economic efficiencies from a maximum-utility (or minimum-cost) perspective. Utility curves of respective schemes are given, and several important phenomena are revealed from the economic analysis. Then we discuss the concrete decision-making models of energy hubs under the three schemes, taking into account the change of electricity price in response to the total demand, which give rise to bilevel optimization problems and are technically transformed into mixed-integer linear programs. Finally, we conduct numerical experiments, which validate the theoretical outcomes, and reveal that the sharing scheme can achieve nearly optimal efficiency without a central organizer, and hence appears to be a promising direction for future multi-energy systems. [ABSTRACT FROM AUTHOR]

Highlights • A multi-agent system is developed to simulate the operation of an energy hub with electric vehicles. • Different penetration rates and various charging patterns of electric vehicle are modelled. • A full random dispatch algorithm is integrated in smart charging strategy. • The maximum capacity and potential of vehicle to grid is calculated. Abstract In this paper, a multi-agent system (MAS) was developed to simulate the operation of an energy hub (EH) with different penetration rates (PRs) and various charging patterns of electric vehicle (EV). Three charging patterns, namely uncontrolled charging pattern (UCP), rapid charging pattern (RCP) and smart charging pattern (SCP), together with vehicle to grid (V2G), were simulated in the MAS. The EV penetration rates (EV-PRs), from 10% to 90% with a step of 20%, are considered in this study. Under the UCP, the peak load increases by 3.4–17.1% compared to the case without EVs, which is the reference case in this study. A main part of the increased electricity demand can be supplied by the gas turbine (GT) when the PR is lower, i.e. 71.7% under 10% PR and 37.4% under 50% PR. Under the SCP, the charging load of EVs is shifted to the valley period and thus the energy dispatch of the EH at 07:00–23:00 remain the same as that in the reference case. When V2G is considered, the electricity demand from the grid becomes the largest in all of the cases, e.g. the demand with 50% PR doubles the electricity demand in the reference case. However, the GT output decreases by 2.9–15.7% at 07:00–23:00 due to the effect of V2G. The variations in the EH's operation further raise the changes in energy cost, i.e. the electricity and cooling prices are lowered by 18.3% and 33.8% due to the availability of V2G and the heating and cooling prices increase by 3.5% and 4.3% under the UCP with the PR of 50%. Regarding the V2G capacity, near 39% of the EVs' battery capacity can be discharged via V2G. In addition, the paper also produced a V2G potential line, which is an effective tool to provide the maximum potential of the EVs for peak shaving at any specific time. [ABSTRACT FROM AUTHOR]

Rapid growth of cities, concerns on global warming and depletion of fossil fuel resources call for sustainable energy solutions for cities. Distributed energy systems such as energy hubs offer promising solutions in this context. Evaluating the energy demand at urban scale is vital to support the design of energy hubs. However, most of the recent studies are based on bottom-up models and do not consider the energy demand in detail. More specifically, the influence of the urban climate on urban energy demand has not been considered so far in the energy system design process. In order to address this research gap, a novel computational platform is developed in the first part of this study, combining an urban climate model with a building simulation tool and an energy system optimization model. The second part of the manuscript is devoted to quantifying the impact of urban climate on energy system design and assessing the consequences of neglecting this specific aspect on energy system performance. Three case studies are conducted considering three building densities for the city of Nablus (building density at the periphery, center and future center of the city) in Palestine. Three scenarios representing (1) standalone buildings (present practice) (2) shadowing and longwave reflection (radiation heat transfer from the walls and the roofs of the buildings to the urban climate and to the sky) of neighboring buildings and (3) urban climate are considered for each case study when computing the energy demand. Subsequently, the energy system is optimized considering Net Present Value (NPV) and system autonomy level as the objective functions (Pareto optimization). The results of the study reveal that the urban climate has a notable impact on the energy demand and energy system design. More importantly, it is shown that the influence of urban climate results in higher fluctuations in the energy demand, which in turn results in a notable increase in the NPV (by up to 40%). This further magnifies the increase in annual or peak demand. The study reveals that neglecting the influence of urban climate in the energy system design process can result in a performance gap in NPV, grid integration level, and greenhouse gas emissions and can impose reliability issues. The design tool introduced in this study can be used for urban planning to mitigate the aforementioned adverse effects. [ABSTRACT FROM AUTHOR]

The electric power, gas, and heat systems work on different but complementary time and space scales. Multiple energy systems (MES) yields an increase on the efficiency and flexibility of energy supply. The coupling between different forms of energy bring difficulties in the planning of the overall energy system. This paper proposes a novel optimal planning method for a community level MES that jointly determines the optimal generation, conversion and delivery of electricity, heat, cooling, and other services. First, configuration planning of a community level MES is introduced and defined using the energy hub (EH) concept. Then, the planning problem is presented with the objective of minimizing the sum of the investment and operating costs, with variables that represent the selection of energy converters or storage and their relationships. The model is then formulated as a mixed-integer linear programming (MILP) problem based on graph theory. The proposed model does not requires any pre-assumptions on the configurations of the EH so that it can plan the MES starting from scratch. Finally, an illustrative example is provided to describe the functioning of our proposed method. A numerical case study for the planning of a subsidiary administrative center in Beijing, China is presented to demonstrate the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

A new reliability assessment approach to Integrated Energy Systems (IESs) is introduced in this paper. The optimal load curtailment (OLC) algorithm and reliability assessment algorithm are both improved in the proposed approach. For the OLC problem, this paper develops a hierarchical decoupling optimization framework for both the energy hub optimal dispatch and the optimal power flow problems. This feasible solution can make the OLC calculation more efficient and accurate. For the reliability assessment algorithm, an impact-increment based state enumeration (IISE) method is accommodated for IESs to accelerate the reliability assessment process. Also, a reduction technique of higher order contingencies is presented for the reliability evaluation of IESs to further enhance the computational efficiency. Case studies are performed on an IESs test case combined the IEEE-33 bus system with 14-node gas system and a practical case combined the IEEE 118-bus power system with Belgian natural gas network Numerical results demonstrate the efficient and robust performance of the proposed approach. Besides, the impacts of energy conversion process and energy hubs on IESs reliability are analyzed in detail. [ABSTRACT FROM AUTHOR]

The benefits of decentralised energy systems can be realised through the optimal siting of distributed energy systems and the design of highly interlinked district heating networks within existing electrical and gas networks. The problem is often formulated as a Mixed Integer Linear Programming (MILP) problem. MILP formulations are efficient and reliable, however the computational burden increases drastically with the number of integer variables, making detailed optimisation infeasible at large urban scales. To tackle complex problems at large scale the development of an efficient and robust simplification method is required. This paper presents an aggregation schema to facilitate the optimisation of urban energy systems at city scale. Currently, spatial and/or temporal aggregation are commonly employed when modelling energy systems at spatio-temporal resolutions from plant scheduling up to national scenarios. This paper argues for solving different scales separately using a bottom-up approach, while keeping track of the error made by reducing the resolution when moving from building to urban scale. Novel modelling formulations and optimisation techniques are presented. They enable drastic reduction of the computational time (by up to a factor of 100) required to find an optimal solution in reasonable time without sacrificing the quality of the results (no more than 1% loss in accuracy). A density-based clustering algorithm enables intelligent division of a large city-scale problem into sub-optimisation problems by creating clusters of different density. In each cluster, the trade-off between centralised and decentralised energy systems and the associated district heating network design is evaluated. A solution is selected based on a local optimisation of the network costs. Demand profiles of each building are assigned appropriately, then at an upper level the energy optimisation problem is solved considering the network losses at lower levels. This method enables large-scale modelling of urban energy systems while taking into account building-scale levels of detail. The clustering method enables assessment of the potential of district heating networks on city scale based on building characteristics and available urban energy systems. [ABSTRACT FROM AUTHOR]

• Bidding strategy based on spectral risk measure is proposed for energy internet zones. • Spectral risk measure is introduced to model decision makers' subjective risk aversion. • Spectral risk measure based strategies are compared with CVaR based model. 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. [ABSTRACT FROM AUTHOR]

Hydrogen-based technologies are one of the pathways toward a carbon-neutral world. This paper proposes a hybrid stochastic-robust framework for the optimal operation of hydrogen-based energy hubs (EHs) for a short-term horizon. The EH operator, as a large consumer, should make decisions to procure the required energy to meet the demands dealing with uncertainties in real-time. To manage the EH efficiently, the EH operator participates in the regulation market (RM) deploying regulation up (RU) and regulation down (RD) actions to compensate for the errors stem from the forecasting procedure in intra-hours. In addition, the EH should support the required hydrogen for hydrogen vehicles (HVs) on a hydrogen refueling station. Power-to-gas unit alongside a gas storage system links the electricity and natural gas network, and it facilitates the heat demand errors in intra-hours using gas-fed boilers. The uncertainty of the demands and the initial state of charge of the HVs are addressed by stochastic programming, while the uncertainty of the RM prices is considered by robust optimization to reach the worst-case realization of the RM prices. The simulation results demonstrate the effectiveness of the proposed framework in managing the hydrogen-rich EH and energy storage systems with the day-ahead and real-time horizons. The amount of 14% error in electricity demands in the intra hours is resulted in 10% increases in purchasing from the RM at the same time. • Hydrogen-Rich Energy Hubs operation in Day-Ahead and Regulation Markets. • Considering hydrogen refueling stations uncertainties in the energy hub operation. • Real-time horizon dealing uncertainty with the forecasting errors in intra-hours. [ABSTRACT FROM AUTHOR]

• Model-free decentralized coordination control of electric vehicle aggregator and energy hub entities is proposed. • Novel multi-agent DRL framework for continuous control is utilized. • LSTM and safety networks are introduced to stabilize the training process. • Developed algorithm exhibits effective and fast control against uncertainties. • Developed method outperforms 3 advanced benchmark algorithms. Energy hub (EH) is an independent entity that benefits to the efficiency, flexibility, and reliability of integrated energy systems (IESs). On the other hand, the rapid emerging of electric vehicles (EVs) drives the EV aggregator (EVAGG) as another independent entity to facilitate the electricity exchange with the grid. However, due to privacy consideration for different owners, it is challenging to investigate the optimal coordinated strategies for such interconnected entities only by exchanging the information of electrical energy. Besides, the existence of parameter uncertainties (load demands, EVs' charging behaviors, wind power and photovoltaic generation), continuous decision space, dynamic energy flows, and non-convex multi-objective function is difficult to solve. To this end, this paper proposes a novel model-free multi-agent deep reinforcement learning (MADRL) -based decentralized coordination model to minimize the energy costs of EH entities and maximize profits of EVAGGs. First, a long short-term memory (LSTM) module is used to capture the future trend of uncertainties. Then, the coordination problem is formulated as Markov games and solved by the attention enabled MADRL algorithm, where the EH or EVAGG entity is modeled as an adaptive agent. An attention mechanism makes each agent only focus on state information related to the reward. The proposed MADRL adopts the forms of offline centralized training to learn the optimal coordinated control strategy, and decentralized execution to enable agents' online decisions to only require local measurements. A safety network is employed to cope with equality constraints (demand–supply balance). Simulation results illustrate that the proposed method achieves similar results compared to the traditional model-based method with perfect knowledge of system models, and the computation performance is at least two orders of magnitudes shorter than the traditional method. The testing results of the proposed method are better than those of the Concurrent and other MADRL method, with 10.79%/3.06% lower energy cost and 17.11%/6.82% higher profits of aggregator. Besides, the electric equality constraint of the proposed method is only 0.25 MW averaged per day, which is a small and acceptable violation. [ABSTRACT FROM AUTHOR]

This paper proposes a new optimization algorithm, namely Self-Adoptive Learning with Time Varying Acceleration Coefficient-Gravitational Search Algorithm (SAL-TVAC-GSA), to solve highly nonlinear, non-convex, non-smooth, non-differential, and high-dimension single- and multi-objective Energy Hub Economic Dispatch (EHED) problems. The presented algorithm is based on GSA considering three fundamental modifications to improve the quality solution and performance of original GSA. Moreover, a new optimization framework for economic dispatch is adapted to a system of energy hubs considering different hub structures, various energy carriers (electricity, gas, heat, cool, and compressed air), valve-point loading effect and prohibited zones of electric-only units, as well as the different equality and inequality constraints. To show the effectiveness of the suggested method, a high-complex energy hub system consisting of 39 hubs with 29 structures and 76 energy (electricity, gas, and heat) production units is proposed. Two individual objectives including energy cost and hub losses are minimized separately as two single-objective EHED problems. These objectives are simultaneously minimized in the multi-objective optimization. Results obtained by SAL-TVAC-GSA in terms of quality solution and computational performance are compared with Enhanced GSA (EGSA), GSA, Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) to demonstrate the ability of the proposed algorithm in finding an operating point with lower objective function. [ABSTRACT FROM AUTHOR]

In this paper, a method for a multi-objective and simultaneous optimisation of building energy systems and retrofit is presented. Tailored to be suitable for the diverse range of existing buildings in terms of age, size, and use, it combines dynamic energy demand simulation to explore individual retrofit scenarios with an energy hub optimisation. Implemented as an epsilon-constrained mixed integer linear program (MILP), the optimisation matches envelope retrofit with renewable and high efficiency energy supply technologies such as biomass boilers, heat pumps, photovoltaic and solar thermal panels to minimise life cycle cost and greenhouse gas (GHG) emissions. Due to its multi-objective, integrated assessment of building transformation options and its ability to capture both individual building characteristics and trends within a neighbourhood, this method is aimed to provide developers, neighbourhood and town policy makers with the necessary information to make adequate decisions. Our method is deployed in a case study of typical residential buildings in the Swiss village of Zernez, simulating energy demands in EnergyPlus and solving the optimisation problem with CPLEX. Although common trade-offs in energy system and retrofit choice can be observed, optimisation results suggest that the diversity in building age and size leads to optimal strategies for retrofitting and building system solutions, which are specific to different categories. With this method, GHG emissions of the entire community can be reduced by up to 76% at a cost increase of 3% compared to the current emission levels, if an optimised solution is selected for each building category. [ABSTRACT FROM AUTHOR]

An optimal day-ahead scheduling method (ODSM) for the integrated urban energy system (IUES) is introduced, which considers the reconfigurable capability of an electric distribution network. The hourly topology of a distribution network, a natural gas network, the energy centers including the combined heat and power (CHP) units, different energy conversion devices and demand responsive loads (DRLs), are optimized to minimize the day-ahead operation cost of the IUES. The hourly reconfigurable capability of the electric distribution network utilizing remotely controlled switches (RCSs) is explored and discussed. The operational constraints from the unbalanced three-phase electric distribution network, the natural gas network, and the energy centers are considered. The interactions between the electric distribution network and the natural gas network take place through conversion of energy among different energy vectors in the energy centers. An energy conversion analysis model for the energy center was developed based on the energy hub model. A hybrid optimization method based on genetic algorithm (GA) and a nonlinear interior point method (IPM) is utilized to solve the ODSM model. Numerical studies demonstrate that the proposed ODSM is able to provide the IUES with an effective and economical day-ahead scheduling scheme and reduce the operational cost of the IUES. [ABSTRACT FROM AUTHOR]

Energy hubs play an important role in implementing multi-carrier energy systems. More studies are required in both their modeling and operating aspects. In this regard, this paper attempts to develop medium-term management of an energy hub in restructured power systems. A model is presented to manage an energy hub which has electrical energy and natural gas as inputs and electrical and heat energy as outputs. Electricity is procured in various ways, either purchasing it from a pool-based market and bilateral contracts, or producing it from a Combined Heat and Power (CHP) unit, a diesel generator unit and Wind Turbine Generators (WTGs). Pool prices and wind turbine production are subject to uncertainty, which makes energy management a complex puzzle. Heat demand is also procured by a furnace and a CHP unit. Energy hub managers should make decisions whether to purchase electricity from the electricity market and gas from the gas network or to produce electricity using a set of generators to meet the electrical and heat demands in the presence of uncertainties. The energy management objective is to minimize the total cost subject to several technical constraints using stochastic programming. Conditional Value at Risk ( CVaR ), a well-known risk measure, is used to reduce the unfavorable risk of costs. In doing so, the proposed model is illustrated using a sample test case with actual prices, load and wind speed data. The results show that the minimum cost is obtained by the best decisions involving the electricity market and purchasing natural gas for gas facilities. Considering risk also increases the total expected cost and decreases the CVaR . [ABSTRACT FROM AUTHOR]

The concerns on energy security and environment protection have driven the need to produce, transform and utilize energy in a more efficient, clean and diversified way. Under this background, we design a paradigm for energy hubs that combine distributed energy supply/combined cooling heating and power (DES/CCHP), renewable energy and energy storage. These energy hubs are comprised of heating, cooling and power systems, and natural gas, power generation and photovoltaic (PV) are the primary energy sources. Also, we propose a paradigm and its operation model for regional multi-energy prosumers (RMEP) whose energy demands are served by interconnected energy hubs. The energy exchange in energy hub is based on the structure of energy buses including power bus, heating bus and cooling bus. These energy buses are interconnected as a ring heating/cooling network and a radial power grid to implement mutually complementary reserves of energy hubs. Moreover, the bi-directional energy flows between prosumer and the main grid are also analyzed. In addition, an optimal scheduling model for RMEP is proposed. The formulated objective of this model is to minimize prosumer’s cost of purchasing electricity and natural gas plus the cost of GHG emission or to maximize the revenue of selling electricity back to the grid, while considering various electricity and gas prices and heating/cooling demands during different time periods. The decision variables for prosumer include the amount of purchased gas, the amounts of purchased and sold electricity at energy hubs. Case studies are undertaken on the 15-node multi-energy prosumer system, where the system comprises three energy hubs. Prosumer’s operational strategies under system normal and contingency conditions on typical summer and winter load days can be obtained. Comparative analyses between mutually independent energy hubs are also conducted. According to the simulation results, prosumer can play an important role in responding to the time-of-use electricity and gas tariffs, shaving the regional peak loads as a whole. Besides, the interconnected energy hubs can enhance the overall system operational flexibility and reliability. [ABSTRACT FROM AUTHOR]

• A thermodynamic network is formulated to model the electrolytic thermo-electrochemical effects. • Geothermal-solar-wind 100% renewable complementarities are proposed for multi-energy supplies. • A mean-variance portfolio scheme is developed to determine appropriate energy generation, conversion, and storage candidates. • The energy risks of high-renewable portfolio are considered. This paper proposes a high-renewable portfolio model of energy hub. In this model, geothermal-solar-wind multi-energy complementarities are fully explored based on electrolytic thermo-electrochemical effects of geothermal-to-hydrogen (GTH), which are converted, conditioned, and coupled through energy hub. The proposed high-renewable energy hub portfolio is an intractable optimization problem due to their inherent strong energy couplings and conflicted energy cost/risk. The original problem is thus characterized through the mean-variance approach to explicitly express the risk associated with the forecast uncertainties. The formulated mean-variance portfolio problem is subsequently modeled as a two-stage mixed-integer nonlinear programming (MINLP) stochastic programming to optimally determine appropriate energy generation, conversion, and storage candidates. Numerical studies on a community microgrid are implemented to verify the effectiveness and superiority of the proposed methodology over conventional wind-solar-battery scheme. Simulations results show that the portfolio cost can be reduced by at most 14.9% with a significantly higher operational flexibility. [ABSTRACT FROM AUTHOR]

This paper describes a method of integrating decentralized energy systems at neighbourhood scale. The method is based on the energy hub concept, which describes and manages the relation between input and output energy flows and thus can be used to optimise energy consumption. The original energy hub concept is further developed to include decentralized and local energy technologies such as photovoltaics, biomass, or small hydro power, together with district heating systems, building and district conversion and storage technologies at neighbourhood level. Additionally, input from a building simulation tool for evaluating time-dependent buildings energy demand is included in the method. The proposed approach can be used to evaluate and size urban energy systems according to their energy-autonomy, economic and ecological performance. The advantage is that the energy supply systems and local energy storage systems can be evaluated in a combined way at district scale. The suggested method allows to lower peaks in energy demands of neighbourhoods on the electrical grid and to reduce the overall consumption. The developed method is finally applied on a case study for which future energy scenarios of different implementation scale are suggested. The area is located in a village in the mountains and contains 29 buildings. Suggested scenarios include decentralized and local renewable energy sources and district and small heating networks. Results show a variation of 52–98 tons of CO 2 emissions, and an energy autonomy between 64% and 92%. In case of CO 2 emissions, the best solution is given for a combination of multiple supply technologies and small networks which shows 46% lower emissions than for a scenario with only PV and biomass based systems. [ABSTRACT FROM AUTHOR]

Nowadays, multi-energy systems are receiving special attention from smart grid community owing to their high flexibility potentials integrating with multiple energy carriers. In this regard, energy hub is known as a flexible and efficient platform to supply energy demands with an acceptable range of affordability and reliability by relying on various energy production, storage and conversion facilities. Given the increasing penetration of renewable energy sources to promote a low-carbon energy transition, accurate economic and environmental assessment of energy hub, along with the real-time automatic energy management scheme has become a challenging task due to the high variability of renewable energy sources. Furthermore, the conventional model-based optimization approach requiring full knowledge of the employed mathematical operating models and accurate uncertainty distributions may become impractical for real-world applications. In this context, this paper proposes a model-free safe deep reinforcement learning method for the optimal control of a renewable-based energy hub operating in multiple energy carries while satisfying the physical constraints within the energy hub operation model. The main objective of this work is to minimize the system energy cost and carbon emission by considering various energy components. The proposed deep reinforcement learning method is trained and tested on a real-world dataset to validate its superior performance in reducing energy cost, carbon emission, and computational time with respect to the state-of-the-art deep reinforcement learning and optimized-based approaches. Moreover, the effectiveness of the proposed method in dealing with model operation constraints is evaluated on both training and test environments. Finally, the generalization performance for the learnt energy management scheme as well as the sensitivity analysis on storage flexibility and carbon price are also examined in the case studies. • A smart energy-hub integrating with multi-energy carriers is investigated. • A novel safe deep reinforcement learning method is proposed. • The proposed method deals effectively with physical constraints. • The proposed method achieves energy cost and carbon emission benefits. [ABSTRACT FROM AUTHOR]

• Part load efficiency curves is successfully added to a linearized energy hub model. • Simplifying the model with constant efficiencies yields errors in total cost up to 3%. • The shape of the part load efficiency curves impacts the total cost of the system. • Heat pumps are used heavily in the system when concurrent heating/cooling loads exist. The equipment part-load efficiency needs to be considered in the design and control of heating and cooling networks as it strongly affects the overall performance. However, optimization models such as energy hubs usually consider constant efficiencies for their energy convertors, causing potentially erroneous predictions. Even though nonlinear programming and mixed integer linear programming formulations have been introduced to optimize hubs with part-load efficiency devices, they typically focus solely on operational costs. Furthermore, the impact of different load profiles on the performance of such hub is not well documented. In this work, the lifetime cost (operation + purchase) of such system was minimized based on an energy hub in which part-load efficiency devices (natural gas boilers, electric heaters, chillers, and heat pumps) were simulated for many different combinations of thermal loads. The model was linearized to reduce computational time and improve the capability of the solver to find solutions. To determine the impact of part-load efficiencies, each device is individually examined while the efficiency of other devices remains constant. The error resulting from assuming a constant efficiency is then calculated based on a reference hub with constant efficiencies. The results indicate a maximum error on the total cost of 1.85%, 0.6%, and 0.16% by assuming constant-efficiency for the boilers, chillers and heat pumps respectively. The loads for which these maximum errors occur are then chosen to optimize the hub with all devices modeled with a part-load efficiency curve. The errors increased to 1.9%, 0.71%, and 3%, respectively. [ABSTRACT FROM AUTHOR]

The design of user-level integrated energy systems is challenged by a variety of combinations of energy converters, complex cascade utilization of multiple energy flows, as well as sequential matching between stochastic energy resources and periodic energy demands. These issues can be addressed by an optimal energy-hub based planning framework considering the synergistic effects under multiple uncertainties. The energy hub model is extended to analyze energy-level matching and source–load balance with time-varying coupling factors representing part-load characteristics. The optimization problem is formulated as a bi-level planning model with uncertainties evaluated by a two-stage global sensitivity analysis. The bi-level planning model determines the system structure and component sizing at the upper level and identifies the optimal operation strategy at the lower level by employing piecewise linearization of part-load characteristics of components involved. The global sensitivity analysis reduces model size with the elementary effect method, identifies the most influential uncertain parameters with a variance-based method. A case study in Beijing is demonstrated for the proposed methodology. The results show that the proposed method can effectively plan the integrated energy system considering sequential source–load matching with the rational scheduling strategy of components. The demand-side response influences the system configuration and renewable energy penetration. Integrating the components' part-load characteristics help avoid the mismatch between the component capacity and energy demand, reducing 6.8% cost compared to the scheme with constant energy efficiency. The three most influential factors identified among 551 uncertain parameters are natural gas price, valley electricity price and nominal efficiency of the gas turbine. • Energy planning framework considering synergistic effects under uncertainties. • Extending energy hub model for energy-level match and source–load balance. • Bi-level linear programming considering components' part-load characteristics. • Global sensitivity analysis performed to analyze multiple uncertainties. • Identifying the most influential factors among 551 uncertain parameters. [ABSTRACT FROM AUTHOR]

[Display omitted] • A universal control method is established for the energy hub. • Hybrid model-driven and data-driven control method combine mechanism and data organically. • A machine learning algorithm is used to extract hidden modes. • Data collection, Learning, Modeling, and Control automatically as external conditions change. • Obvious energy-saving effect when the method is applied to the energy hub. Energy forms the foundation for humans, but with the wastage of energy and energy consumption being at critical levels, energy saving has become an urgent priority. As a general term for various complex energy systems, energy hub and its control have become key research objects with regard to energy saving. In terms of control of an energy hub, the traditional model-driven approach and the currently rapidly developing data-driven approach have their own characteristics. In this paper, we propose a hybrid model-driven method and a data-driven control method using machine learning algorithms to combine the characteristics of the two driven approaches to realize the extraction of the data hiding mode. The Koopman operation is used to increase the dimension of the data to be linearized. Subsequently, the singular value decomposition method decomposes the data in polar coordinates, reducing dimensionality while ensuring linearization. The polynomial model obtained through machine learning training is simple and flexible. The online data of the energy hub can be used for fast coefficient fitting, and the speed and accuracy of the model can be guaranteed when external conditions change. Case studies on a circulating cooling water system show that this method could complete the process of data collection, learning, modeling, and control automatically when external changes occurred, and the time required for the entire process could meet the needs of a control response. It could rapidly and accurately complete the control process as well as effectively reduce the energy consumption, and it did not generate excessive control costs in the process. [ABSTRACT FROM AUTHOR]

Electric vehicle aggregator (EVAGG) is an independent entity that facilitates exchanging electricity between electric vehicles (EVs) and the grid. Energy hub (EH) is another independent entity playing a remarkable role in enhancing the efficiency, flexibility, and reliability of multi-energy systems. Although interacting between various agents is beneficial to enhance their capability, it is challenging to schedule such interconnected entities. In this paper, EVAGG and EH, as independent entities, are scheduled independently and only exchange the information of electrical energy. The EVAGG scheduling is a function of EV owners' driving patterns, including EVs' arrival and departure times and the initial state of charge. Besides, both the EVAGG and EH operations are affected by the uncertainty of the locational marginal prices. Hence, this paper proposes a hybrid decentralized robust optimization-stochastic programming (DRO-SP) model based on the alternating direction method of multipliers to coordinate the management of entities. Stochastic programming is used to model the uncertainties of the EVs patterns, while the uncertainties of the locational marginal prices are modeled via robust optimization to grasp the worst-case realization. Simulation results demonstrate the effectiveness of the proposed hybrid DRO-SP in terms of economic scheduling the entities while guaranteeing information privacy between entities. • Proposing a framework for decentralized operation of energy hub and EV aggregator. • Presenting an uncertainty budget model for electricity prices uncertainty. • Proposing a hybrid model for the uncertainties of the EV aggregator problem. • Investigating the convergence procedure in ADMM algorithm. [ABSTRACT FROM AUTHOR]

• A decentralized scheduling method is proposed for the optimal operation of integrated community energy systems. • The decentralized optimization problem is solved via an improved consensus-based alternating direction method of multipliers (ADMM). • Better convergence performance can be achieved by pre-defining the decreasing direction of residuals and dynamic updating the step size. • Less computation expense can be achieved under not only the normal operation but also the information loss situation. To transform the original centralized scheduling framework of integrated community energy systems (ICES), a decentralized scheduling method is proposed for the optimal operation of ICES. Firstly, linearized mathematical models of the electric distribution system and natural gas distribution system are established to eliminate the nonconvexity and nonlinearity of the multi-energy systems. Meanwhile, an improved energy hub model is proposed by introducing the state variable, which is able to obtain the optimal solution with better accuracy and higher efficiency. Secondly, a decentralized scheduling framework is developed via an improved consensus-based alternating direction method of multipliers (ADMM). By introducing several coupling links as consensus variables, the optimal operation of the ICES is decoupled according to the physical interactions among each sub-system, in which the original model can be solved parallelly in a decentralized manner. Only the consensus variables are exchanged among sub-systems so that the privacy and confidential items of each sub-system operated by different entities can be ensured. Finally, two different ICES test cases with different system scales are utilized to demonstrate the effectiveness of the proposed method, which is able to provide scheduling scheme same as the centralized method. Dynamic step size modification is further considered in the consensus-based ADMM. By updating the step size during each iteration, the computation expense of optimization process is reduced with less consuming time, iteration and apparent oscillation, by which the convergence performance of ADMM is improved with higher computation efficiency. [ABSTRACT FROM AUTHOR]

• Energy Hub optimization of asset design and operation to supply data-driven demand. • Novel comparison of distinct asset sets regarding total cost and LCA emissions. • Purely stationary asset upgrades are preferential to pure mobility upgrades. • Joint optimization of MES and EVs enables large CO 2eq mitigation at negative costs. • Uncertainty analysis highlights robust benefits of cross-sector optimization. Residential energy and mobility demand are responsible for a substantial share of global greenhouse gas emissions due to high dependency on fossil fuels in heating and motorized individual transport. Technology upgrades might enable cost-effective climate change mitigation in decentralized mobility-including Multi-energy Systems (MIMES). Their holistic and cross-sectoral optimization with respect to design and operation can innovatively identify tradeoffs between ecological and economical solutions, quantify potential benefits and show drawbacks over current solutions. To this end, this work provides a novel, hourly-resolved, consumer-centric, multi-objective optimization framework based on the Energy Hub concept that includes private mobility investments. It compares four distinct technology portfolios for minimal lifecycle emissions and total annualized cost when supplying residential demands to promote beneficial solutions. In a case study, consumers may modify their combustion-based heating and mobility systems (1) by switching to e-mobility (2), by switching to stationary Multi-energy Systems (3), or by switching to e-mobility and Multi-energy Systems simultaneously (4). Optimizations on 83 stochastically selected single- and multi-family buildings in St. Gallen, Switzerland, demonstrate that all three technological upgrades offer improved performance over (1): While costs decrease moderately in cost-driven optimizations, emission reductions range from 16% up to 68% when emission-driven optimizations are performed. The joint electrification of stationary and mobile assets (4) is particularly attractive and outperforms all other technology cases. Scenario (3) offers the second-best performance. While the optimal design and operation of assets depend on the technology availability, emission reduction targets, building size, and demand properties, an uncertainty analysis underpins the overall benefits of upgrades and accredits robustness to abovementioned ranking for wide techno-economic parameter variations and objectives. Pathways for further emission reductions are additionally shown, and the increased initial cost and seasonal dependence on the electricity grid are discussed as hurdles for a widespread implementation of economical and climate-friendly MIMES. [ABSTRACT FROM AUTHOR]

• A novel look-ahead risk-constrained framework is proposed. • Day-ahead and look-ahead scheduling strategies of the energy hub are proposed. • The proposed framework can effectively reduce the operational cost of the energy hub. • Demand response programs increase system flexibility and reduce operational cost. Energy hubs or multi-energy systems facilitate an improvement in their efficiency and flexibility for different energy supplies. However, the application of energy hubs results in scheduling challenges for the entire system due to the mutual impact of various energy forms and the increase in the number of uncertain variables. One of the parameters of an energy storage system, which is an indispensable component of an energy hub, is the state-of-charge at the end of the first day of scheduling. This parameter is crucial because its final state represents the initial state-of-charge for the second day, which has an effect on the operational cost of the second day. Based on a look-ahead risk-constrained technique, this work investigates the optimal scheduling of an energy hub for two days with the aim of minimizing the total operational cost. The state-of-charges of an energy storage system at the end of the first day is optimized by considering the scheduling result on the second day. The uncertainties of the two days are demonstrated via various scenarios. Additionally, a demand response program is adopted to reduce the total operational cost and increase the system flexibility. The results validate the feasibility of using look-ahead risk-constrained scheduling in an energy hub and indicate that a reduction in the total operational cost is achieved. [ABSTRACT FROM AUTHOR]

• Peer-to-peer transaction is implemented for multiple energy hubs operation. • Cooperative game is used to develop convincing payoff allocation. • Both mathematical model and algorithm for payoff allocation are provided. • Two test systems are applied to validate effectiveness of proposed method. Energy hub (EH) has attracted worldwide appeals and shows broad prospects in promoting high-level renewable energy integration as well as supplying various loads with high flexibility. Although the cooperation of multiple EHs will further improve the flexibility and economy, how to allocate the payoff fairly and convincingly remains challenged. To address the above challenge, this paper provides a market mechanism based on EH's peer-to-peer (P2P) transaction for the operation of multiple EHs. The cooperative game theory is applied to develop a fair and convincing payoff allocation scheme. This paper further proves that the cooperative game of multiple EH operations is a balanced game, demonstrating EHs' grand coalition's stability. The payoff allocation problem is formulated as a two-stage problem. In the first stage, payoff allocation is determined to minimize the worst-case excess. In the second stage, the coalition with the highest excess is found and returned to the first-stage problem. To solve the two-stage problem, the solution technique utilizing column constraints generation is provided. Numerical results for two test systems show the effectiveness, computation performance, and scalability of the proposed method. In the test system, each EH has its payoff increased, ranging from 4% to 19%. The proposed solution technique avoids the computational burden growing exponentially and computes 4168 s when the number of possible coalitions is up to 4.29e9. [ABSTRACT FROM AUTHOR]

• A hierarchical structure of a community integrated energy system is proposed. • A tri-level game model is proposed to design distributed transaction mechanism. • Vertical trading is formulated as a dual-Stackelberg game. • Horizontal bidding among the energy hubs is modeled as a non-cooperative game. • Game equilibrium is proven to exist and solved by the distributed algorithm. As the energy market shifts from vertically integrated structures to interactive competitive structures, the transaction mechanism for resident users faces significant challenges because of its large numbers and small size. This paper proposes a distributed coordinative transaction mechanism of a community integrated energy system utilizing a tri-level game model. The vertical trading aspect is formulated as a dual-Stackelberg game, wherein the load aggregators serve as an intermediary between the energy hubs and resident users. Horizontal bidding among the energy hubs is modeled as a non-cooperative game, wherein each stakeholder pursues the optimal benefit. The equilibrium of this tri-level game is proven to exist and is solved by means of a distributed algorithm. The tri-level model is initially transformed into a bi-level problem through the Karush–Kuhn–Tucker condition and convexification, and is then solved using the distributed iterative algorithm. Finally, the effectiveness of the proposed tri-level game model and distributed solution algorithm was verified by case study, which confirmed that all stakeholders could benefit from the proposed transaction mechanism. [ABSTRACT FROM AUTHOR]

Centralized planning of the electricity network, natural gas network and energy hubs (EHs) implicitly assumes a vertically integrated structure, which ignores the common independent ownership of different systems. A more practical approach should differentiate the electric system, gas system and each EH as separate stakeholders, and establish a distributed planning framework. Such a distributed planning framework based on the alternating direction method of multipliers (ADMM) is proposed in this paper, which uses the amount of electricity and natural gas required by EHs from each node as the decoupling information, and decomposes the joint planning problem into multiple planning sub-problems, respectively for the electric system, natural gas system and each EH. To reflect the operation more accurately in the planning stage, unit commitment is embedded in the planning model as its operation module. Finally, the proposed method is verified on an illustrative system composed of a modified IEEE RTS 24-bus electric system, Belgian 20-node natural gas system and four EHs. The case study demonstrates the impacts of unit commitment, gas price and penalty parameters on the planning scheme and the number of iterations. • An (n + 2) -agent distributed planning for one electric system, one natural gas system, and n energy hubs. • Distributed siting of energy hubs alongside planning of electric transmission lines and gas pipelines. • Unit commitment embedded in the planning framework to enhance operational flexibility. • Distributed planning with ADMM approaching the performance of the centralized one. [ABSTRACT FROM AUTHOR]

• A model is presented for an ice storage performance inside an energy hub. • Energy hub uses stochastic programming for optimal operation. • There are renewable energies including wind turbines and solar panels. • There are uncertainties in scheduling of energy hub. • The proposed solution is based on scenario generation and scenario reduction. The energy hub as a new concept has attracted much attention in modern power systems. One of the aspects of an energy hub deals with its optimal operation. Energy hub scheduling for a day-ahead time horizon including demand response program, different kinds of energy storage, and renewable energy resources, are focused on this current study. In particular, the effects of ice storage, as a novel and developing storage device and yet researchable subject, on the performance and efficiency of the energy hub operation cost are investigated. The stochastic behavior of ice storage is also considered to be compared with deterministic conditions. The studied energy hub is composed of energy converters, including combined cooling, heating, and power (CCHP), to deliver energy to its electrical, heating, and cooling loads. It uses clean, green and, renewable energies as wind turbines and solar panels. The method applied is that the studied energy hub minimizes its operation costs while satisfying demand response constraints in an uncertain environment. The proposed methodology has been evaluated in comparative case studies, and the obtained results show the requirement of including uncertain mode of ice storage in the energy hub. [ABSTRACT FROM AUTHOR]

• Optimal configuration of the two representative district MESs in southern and northern China. • Up to 80% of CO 2 can be reduced and more than 40% renewable penetration can be achieved by the MES. • The average cost of reducing CO 2 rises from 491 CNY/tCO 2 to 880 CNY/tCO 2 for the Tongli system. • The cost of reducing CO 2 is higher in southern China than in northern China under the same target level. Multiple energy systems (MES) can exploit synergies among different energy sectors to optimize overall efficiency. They can accommodate renewable energy sources and reduce carbon emissions more economically than in systems where each energy sector is planned and operated separately. This paper describes the optimal planning of two real-world district MESs in China: Tongli new energy town and Tongzhou subsidiary administrative center. In addition to the conventional economy objective, two kinds of low-carbon targets, including the carbon emission target and renewable penetration target, are taken into account. The results show that investment decisions on coal-fired units, combined heat and power plants and renewable energy sources are significantly influenced by the choices of different targets. Reducing carbon emissions and increasing renewable energy integration generally work in tandem, but result in a higher planning cost. The average cost of reducing carbon emissions by 40% is 528 CNY/tCO 2 in Tongli and 327 CNY/tCO 2 in Tongzhou. Meanwhile, the average cost of achieving a 25% penetration of renewable energy is 468 CNY/MWh in Tongli and 363 CNY/MWh in Tongzhou. As expected, the cost of carbon reduction and renewable energy integration gradually rises as the targets tighten. Only a small fraction of the available renewable energy is curtailed (2.9% in Tongli and 8.3% in Tongzhou) even when the renewable penetration reaches 40% in both two systems. Finally, a sensitivity analysis illustrates the effect of planning parameters, including the carbon price, the capital cost of energy components and the fuel prices, on the planning results. [ABSTRACT FROM AUTHOR]

• A regional integrated energy system security region (RIESSR) model is developed. • A practical RIESSR model based on simplified security constraints is established. • The preventive control model based on the practical RIESSR is built. • The optimizing control model based on the practical RIESSR is built. With the deep interconnection of multi-energy flow in the regional integrated energy system (RIES), the security problem after contingencies has gained wide attentions. Compared with the traditional time-consuming 'point-wise' simulation method, the 'region' method has become a new powerful approach to analyze the system security status, which can be calculated offline and applied online. Nowadays, the rapidly development of RIES automation level has laid foundation for the load transfer after faults. Therefore, faced with the RIES supplied by energy hubs (EHs), this paper proposes the concept and model of regional integrated energy system security region (RIESSR) based on the N − 1 security guideline. Furthermore, a practical RIESSR model is simplified to improve the solution efficiency, including the security boundary, security distance and security margin. In order to realize the RIESSR visual observation, a simulation fitting solution of practical RIESSR security boundary is presented. Based on the RIESSR, the framework of RIES security analysis is constructed to achieve the measurement of system status, obtaining the security information and controlling the energy generation of EHs. On this basis, the preventive control and optimizing control models are established to optimize the system security status. Finally, some numerical cases are simulated to verify the effectiveness and applicability of the proposed RIESSR model. The case study demonstrates that the security boundary of RIESSR could be described and the two-dimensional and three-dimensional visualizations of practical RIESSR are realized. Through the RIESSR, the analysis of security assessment and energy supply capability can be conducted rapidly and directly. After the preventive control, the insecure operating point could be adjusted to the secure status quickly. According to the demand of different security degree and efficiency degree, the set of Pareto fronts of optimizing control measures are presented, which provides guideline for the dispatchers. [ABSTRACT FROM AUTHOR]

• A novel optimal load dispatch model for a community energy hub is proposed. • Both electrical and thermal demand response programs are taken into account. • The proposed model considers the random access of large-scale electric vehicles. • A robust optimization approach is proposed to deal with the price uncertainty. As an important segment in the multi-energy systems, energy hub plays a significant role in improving the efficiency, flexibility and reliability of the multi-energy systems. In addition, load dispatch is an important optimization problem in the energy system, which has great significance to reduce energy consumption, environmental pollution and user's energy costs. In this regard, this paper proposes an optimal load dispatch model for a community energy hub, which aims to reduce the total cost of community energy hub, including operation cost and CO 2 emission cost of the system. In the community energy hub, the combined heat and power (CHP) unit, gas boiler, heat storage unit, photovoltaic (PV) array, wind turbine (WT), and electric vehicles (EVs) are included. The uncertainties of EVs are modeled using the Monte Carlo simulation, and a robust optimization approach is adopted to deal with the future electricity price uncertainties. In addition, the proposed model comprehensively considers both electrical and thermal demand response (DR) programs. In the paper, three scheduling scenarios with different EV charging/discharging and DR settings are discussed. The simulation results show that the total costs can be effectively reduced by adopting coordinated charging/discharging mode for EVs. Meanwhile, the results also reveal that the consumers' total cost can be further reduced by implementing the DR programs. [ABSTRACT FROM AUTHOR]

• Multi-generation heat pump systems provide savings in heating/cooling networks. • Optimal heat pump size based on cost is ∼80% of the maximal thermodynamic limit. • Putting emphasis on GHG emissions does not change the optimal use of heat pumps. • To reduce cost and GHG emissions, electricity outperforms natural gas for heating. Waste heat recovery can help reducing operation costs and greenhouse gas emissions. In the present work, an "energy hub" template was employed to design combined heating and cooling networks in which heat pumps can be used to recover heat from the cooling loop and supply it to the heating loop. Heating and cooling loads of the network can be satisfied by natural gas boilers, electric boilers, chillers, and heat pumps. The design of the system and its operation over the year were optimized with respect to cost and greenhouse gas emissions under different combinations of heating and cooling loads. The introduction of 8760-h synthetic loads allowed covering several possible load profiles driving the energy hub. The contribution of each possible energy source and technology and the sizing of the heat pump system are optimized, while ensuring satisfaction of the heating and cooling demands. The optimized hub configurations for scenarios with and without waste heat recovery were compared, showing that heat pumps were beneficial in all scenarios. The optimal capacity of heat pumps to minimize total cost was found to be ∼80% of the maximal possible value from a thermodynamic analysis of the loads. The simultaneous minimization of cost and emissions revealed a relatively sharp transition from gas to electric heating as more emphasis is put on emissions than cost, but in all cases, waste heat recovery with heat pumps was heavily used to satisfy the heating and cooling loads. [ABSTRACT FROM AUTHOR]