A Comprehensive Review of the Design and Operation Optimization of Energy Hubs and Their Interaction with the Markets and External Networks
Abstract
:1. Introduction
1.1. The Background and the Sector Coupling Need
1.2. The Energy Hub Concept at the Service of Need
 Distributed generation technologies: renewable technologies to decarbonize energy supply systems;
 “Enduser” sector coupling technologies: energy conversion technologies for the electrification of the enduses that enable the flexibility of endusers/prosumers to be activated;
 “Crossvector” sector coupling technologies: technologies that allow the integration of multiple energy carriers. The main technology that can be easily implemented in most energy hubs is the CHP, which can be installed both at the prosumer level (buildings, shopping centers, industries) and at the city/neighborhood level (district heating);
 Energy storage technologies of different energy carriers (electrical, thermal, mobility).
1.3. The Need for This Review and Its Contribution
 Analysis of the technologies and energy carriers in EHs;
 Analysis of the design and operation optimization of EHs, considering the full chain of relevant topics, i.e., problem formulation with constraints, objective functions overview, multiobjective approach and solution methodologies, solvers and modeling frameworks considering heuristic methods, uncertainty, and risk aversion, management of flexibility sources, and simulation methods for electric vehicles (EVs);
 Analysis of the EHs’ interaction with multiple markets, from energy and balancing markets to peertopeer (P2P) markets, along with business models and interaction of EHs with the external network; and
 Analysis of collateral aspects such as temporal and spatial scopes.
2. The Methodology Used for This Review Paper
 Collect all the documents related to optimization problems, including multiobjective approaches, multicarrier energy systems, and EHs configurations. In detail, 128 related documents were collected from the most popular and impactful research repositories of the research and innovation (R&I) community;
 Identify a list of topics of interest to focus on. The EH concept is a multifaceted research question that entails different topics to be further investigated. Therefore, an exhaustive list of 18 topics of interest that are related to EHs has been developed. Within this list, the topics have been further categorized as “setting the background” topics or/and “research and innovation” topics. Background topics are the ones that formulate the state of the art of this review and establish the baseline knowledge of this effort, whereas “research and innovation” topics are classified as such to formulate further innovation pathways and the research questions that are analyzed in detail. Of course, a topic can be characterized as both “background” and “research and innovation”;
 An extensive review of the topics for each of the documents in order to capture the holistic approach of this review and the connection of EHs with the external framework, such as the networks, the market, and the business models; and
 Compilation of brief reports per topic for both stateoftheart and innovation approaches before developing this review.
3. The EH Configuration in the Literature
3.1. Energy Carriers of an EH
3.2. Cluster of EHs
 Advanced security of energy supply;
 Increased provision of system services to neighboring systems, such as balancing and ancillary services;
 Reduced RES curtailment and therefore reduced GHG emissions;
 Increased system reliability;
 Increased load flexibility;
 Selfsufficiency and minimization of costs related to energy exchange with the upper grid.
 Cost of the required infrastructure and the connecting technologies;
 The ownership of the interconnected networks has to be adequately defined;
 Advanced communication, data acquisition, and management infrastructure is needed for the optimum operation of the interconnected networks;
 The high initial investment with a long time for payback;
 Lack of cases and proper business models;
 Lack of regulations regarding functionalities and operation, including roles and responsibilities;
 Public acceptance of the interconnection and interaction between the EHs.
3.3. Energy Conversion Technologies
3.4. Energy Storage and Flexibility Potential
3.5. Flexibility Potential of EVs
3.6. Key Challenges for the EH Configuration in the Literature
 Limited connection between various energy sectors/carriers. Although may diverse carriers can be present in the different EHs structures, the interconnectivity among them is low in many cases. This means that the full potential of employing the advantages of the integration as described in the introduction remains unused.
 Limited economic incentives in order to encourage the use of flexibility, focusing only on the electricity carrier. This results in a limited number of technologies participating in flexibility management, such as batteries, whereas the thermal part and their flexibility potential are neglected in most cases;
 Management or/and pricing schemes of other energy carriers beyond electricity. This results in more complex and decentralized schemes for energy carriers other than electricity that are now not represented;
 The inclusion of EVs in ILECs can make their management more difficult. Even if there is some coordination, there will always be several more constraints than for a simple battery storage system;
 The stochastic nature of EV operation that intertwines with behavioral aspects can affect the stability of the system as well in case of high EV penetration.
4. Objective Functions for Optimal Design and Operation of EHs
5. Optimization Problem Constraints
6. Optimization Problems Modeling
6.1. MILP
6.2. MINLP
6.3. MILP & MINLP
6.4. Key Challenges for the Optimization Problems Modeling
7. MultiObjective Optimization Problems and Methods
 The preferencebased approach in which the decisionmaking is performed before the optimization. This approach requires a good knowledge of the preferences of decisionmakers that need to be respected in the optimization problem formulation. Quantifying these preferences is a challenge;
 The second approach is considered ideal. The optimization is performed before the decisionmaking. This approach is more desirable than the previous one, as it is less subjective and leaves the final decision to the decisionmakers.
8. Heuristic Methods
9. Optimization Solvers and Modeling Environments
10. Uncertainties and Risk Aversion
10.1. Uncertainties
 Stochastic optimization discretizes the continuous stochastic parameters into a tree of scenarios, whose nodes of uncertainty are assumed to be known;
 Robust optimization defines the solution according to more adverse scenarios regardless of the probability of occurrence;
 Chanceconstrained optimization introduces probabilistic constraints for obtaining a tradeoff between the optimal value and the robustness of the solution.
10.2. Risk Aversion
 The first one considers risk metrics that provide a grade of risk to moderate the decision;
 The second one is through distributionally robust optimization.
10.3. Key Challenges in Handling Uncertainties and Risk Aversion
11. Interaction of EHs with Multiple Markets and Networks
11.1. Involvement of EHs in Multiple Wholesale Markets
11.2. P2P Markets
11.3. Key Challenges for the Interaction of EHs with Multiple MARKETS and Networks
12. Business Models of EHs
13. Other Collateral Concerns
13.1. Temporal Scope for the Operation Optimization of EHs
13.2. Spatial Scope for the Design and Operation Optimization of EHs
14. Discussion and Conclusions
14.1. Summary of Challenges and Limitations
14.2. Research Pathways
 Different types of EH configurations with a wide range of conversion technologies that compile general solutions and can be replicable and scalable should be considered. Energy storage and its flexibility potential are of high importance, and EHs’ future configuration models need to consider the optimization of their sizing and placement, including potential alternative means of storage such as EVs and hydrogen. On top of that, as the interaction of different energy carriers affects the nonlinearity and nonconvexity of the problem, a more complex configuration shall lead to higher sensitivity of initial conditions that affect the problem formulation and solution. This challenge should be faced as the integrated grid approach needs the high interconnection of many different carriers;
 The optimization of the design and operation of an EH is a complicated task that has been considered in the past under different prisms as already analyzed. It is also well established that in order to address the needs of the most related actors in an EH, the multiobjective approach is the way forward. What is the most challenging so far is the accurate representation of physical phenomena that would add complexity to an already multifaceted problem. Therefore, it seems that the optimization of design and operation should be tackled in layers and in a distributed way with loops of interaction that would allow the different layers to be in accordance. The layers could address physical carriers and/or hierarchical layers of governance where the complexity is built based on the pursuit of the actors involved. As an example, distributed optimization could be dealt with within the EH on the prosumers’ side while being in good collaboration with the central optimization at the central level of the ILEC. An example of this innovative approach is tested under the approach proposed in the eNeuron H2020 project (November 2020–October 2024, ID: 957779), which has the main goal to develop an innovative toolbox for the optimal design and operation of local energy communities, integrating DERs and multiple energy carriers at different scales;
 Last but not least, regarding the EHs’ interaction with the markets, a further necessary step would be the specificcountry cost–benefit analysis feeding sustainable business models. Moreover, further research is required on how EHs may interact with multiple energy markets, assuming knowledge of grid network constraints, considering the market design, and evaluating mutual coupling with other energy carriers and markets, including gas markets. Especially for P2P markets, when considering users’ interaction, local but centralized resources, as well as different energy carriers, should be involved. A good baseline for further investigation under the EH scope could be the consideration of the electricity–carbon integrated P2P market as presented in [132].
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviation
Acronym  Meaning  Acronym  Meaning 
AC  Air conditioners  LMO  Local market operator 
ADMM  Alternating direction method of multipliers  LP  Linear programming 
B2B  Businesstobusiness  MAGA  Multiagent genetic algorithm 
B2C  Businesstoconsumer  MBFO  Modified bacterial foraging optimization 
BS  Bill sharing  MECS  Multienergy coupled systems 
C2C  Consumertoconsumer  MES  Multienergy system 
CAES  Compressed air energy storage  MGEM  Microgrid energy management 
CD  Crowding distance  MILP  Mixedinteger linear programming 
CES  Community energy storage  MINLP  Mixedinteger nonlinear programming 
CHP  Combined heat and power  MIQCP  Mixedinteger quadratically constrained 
DAM  Dayahead electricity market  MISOCP  Mixedinteger secondorder cone programming 
DDRO  Datadriven distributionally robust optimization  MMR  Midmarket rate 
DE  Differential evolution  MOEA  Multiobjective evolutionary algorithms 
DER  Distributed energy resource  MOMFEAII  Multiobjective multifactorial evolutionary algorithm II 
DHN  District heating network  MPC  Model predictive control 
DRO  Distributionally robust optimization  MSGAII  Multistrategy gravitational search algorithm 
DRP  Demand response programs  MTLBO  Modified teaching–learningbased optimization algorithm 
EA  Evolutionary algorithms  NR  Nondominated rank 
EH  Energy hub  NSGA  Nondominated sorting genetic algorithm 
EHGHS  Electricity–hydrogen–gas heat integrated energy system  nZED  Netzero energy districts 
ESP  Energysharing provider  O&M  Operation and maintenance 
ESS  Energy storage system  OEF  Optimal energy flow 
ETIP SNET  European Technology & Innovation Platforms Smart Networks for Energy Transition  P2G  Powertogas 
EU  European Union  P2P  Peertopeer 
EV  Electric vehicle  PCC  Point Of common coupling 
GA  Genetic algorithms  PCM  Phase change materials 
GfG  Gasfired generation  PHEV  Plugin hybrid electric vehicle 
GHG  Greenhouse gas  PRIMES  Priceinduced narket equilibrium system 
GIL  Grid integration level  PSO  Particle swarm optimization 
HER  Heattoelectricity ratio  PV  Photovoltaic 
HES  Hybrid energy system  QPSO  Quantum particle swarm optimization 
HP  Heat pump  RCGA  Real coded genetic algorithm 
HVAC  Heating, ventilation, and airconditioning  RES  Renewable energy sources 
ICC  Integer cut constraints  RMILP  Robust MILP 
IDM  Intraday market  SDR  Supply and demand ratio 
TOU  Time of use  
IGDT  Information gap decision theory  V2G  Vehicletogrid 
ILECs  Integrated local energy communities  VIKOR  Vlsekriterijumska optimizacija i kompromisno resenj multicriteria decisionmaking method 
KPI  Key performance indicator  VRE  Variable renewable energy 
LAES  Liquid air energy storage  WC  White certificates 
LEC  Local energy community  ZCMES  Zerocarbon multienergy system 
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Categories  Main Advantages of EHs 

Technical advantages  Enhanced efficiency for energy islands, i.e., systems with weak or no interconnections with the upstream grid; No size limitation. The size of an energy hub can vary from a building level (single house) to a community level (city—island); Increased system reliability; Increased load flexibility; 
Economic advantages  Reduction of operating costs; Reduction of electrical grid congestion; 
Environmental advantages  Reduction in GHG emissions; Reduction of fossil fuel use with the increased renewable energy penetration; Increase in energy efficiency. 
Year  Total Number of References  Percentage [%] 

2023  2  1.52 
2022  11  8.33 
2021  27  20.45 
2020  23  17.42 
2019  25  18.94 
2018  11  8.33 
2017  7  5.30 
2016  6  4.55 
2015  7  5.30 
2014  4  3.03 
2013  3  2.27 
2012  3  2.27 
2011  0.00  
2010  2  1.52 
2009  0.00  
2008  0.00  
2007  0.00  
2006  1  0.76 
References  Energy Carrier Combinations  

Electricity  Heating/Cooling  Hydrogen  Natural Gas  Domestic Hot Water  
[18,19,20,21,22,23,24,25,26,27,28,29,30,31]  √  √  
[32]  √  √  √  
[33,34,35,36,37,38]  √  √  √  
[39,40,41,42,43,44]  √  √  √  √  
[45]  √  √  √  √ 
Energy Carrier  

Electricity  Heating/Cooling  Hydrogen  Natural Gas  Domestic Hot Water  
Technology  PV systems [23,27,38,40,41,43,44,45,48,51,52,53,54,55,56,57,58,59,60,61]  CHP [12,14,17,19,21,22,23,24,25,26,27,28,29,30,31]  H_{2} generator from fossil fuels [50]  P2G [23]  Heat recovery from CHP [33,34,35,36,37,39,40,41,42,43,44,52,59] 
Wind turbines [48,51,53,61]  Gas boilers [14,19,20,21,23,24,25,27,29,31,32,38,45]  H_{2} electrolyzer (P2G) [18,20,23]  Methanation processes and devices (biogas) [28,49]  Solar thermal [39,40,41,44,58,62]  
Solar thermal [39,40,41,44,58,62,63]  Heat Pumps [19,21,22,25,26,27,29,31,32,45]  Gas boilers [38,61,64]  
Biomass [33,34,39,40,41,52]  Absorption chillers [21,25,26,29,31,32,45]  Biomass boilers [39,40,41,43,44,59]  
Diesel generators [65,66,67,68]  Electric boilers [69]  
Fuel cells [58,70] 
Energy Carriers  

References  Electricity  Heating/Cooling  Hydrogen  Natural Gas  Domestic Hot Water  
Storage facilities  [27,29,51,56,65,67,81]  Batteries  
[21,24,25,26,30,31,38,41,43,55,57,61,71,75,78,79,82,83,84,85]  Batteries  Thermal Storage  
[72]  Batteries  Thermal Storage  H_{2} storage  
[20]  Batteries  Thermal Storage  H_{2} storage  Natural gas storage  Thermal Storage  
[18,23]  Batteries  H_{2} storage  
[40,44,64]  Batteries  Thermal storage  Thermal storage  
[74]  Batteries  Thermal storage  Natural gas storage  
[80]  PHS  Thermal storage  Natural gas storage  
[54]  Batteries, EV  Thermal storage  
[73]  Batteries, CAES  
[32]  Batteries, CAES, PHEVs  Thermal storage  H_{2} storage  
[58]  Batteries, CAES  Thermal storage  H_{2} storage  Thermal storage  
[19]  PHEV  Thermal storage  
[76]  Flywheel, batteries, CAES  
[42,86]  Thermal storage  
[69]  Thermal storage  H_{2} storage  Thermal storage  
[70]  H_{2} storage  
[87]  Natural gas storage  
[88]  Thermal storage  
[45]  V2G EVs 
Reference  MultiObjective Optimization Functions 

[88,95] 

[94] 

[78] 

[79] 

[39,41,84] 

[40,42,70,82,96] 

[54,93] 

[43] 

[44,58,59,62] 

[65] 

[30] 

Reference  SingleObjective Optimization Function 

[55]  Maximization of the EH/aggregator operator’s profit; 
[23,27,32,41,45,57,81,92,97,98,99,100]  Minimization of operational cost; 
[51]  Maximization of the utilities of the customers in a P2P energysharing trading; 
[52]  Maximization of the social welfare given by the sum of the profits of all participants in the P2P energy trading; 
[56]  Minimization of the total energy expenditure of all individual customers in the EH; 
[91]  Minimization of the total social energy cost to derive the optimal energysharing profiles for the building cluster; 
[28]  Minimization of the cost of device operation, energy storage, energy transaction, and curtailment power of wind and PV; 
[29]  Minimization of the total investment cost and total operating costs of energy technologies in the EH. 
Technology Constraints  References 

Operational constraints (e.g., capacity constraints, ramprate constraints, storage constraints)  [19,21,22,23,24,25,26,27,28,29,30,31,32,33,35,36,37,38,39,41,42,43,44,47,49,50,51,54,55,56,57,58,59,60,61,62,65,66,67,68,69,70,71,72,74,75,76,77,78,79,80,81,82,83,84,85,86,88,90,92,93,94,96,99,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116] 
Design constraints (e.g., device availability and available sizes in the market)  [41,43,44,85] 
Selection of the technologies in the configuration  [58,105,117,118] 
Network Constraints  Description  References 

Network flow constraints  Electricity (active and reactive power), gas, heating, cooling, domestic hot water, energy flows  [21,23,24,25,27,32,35,36,39,40,41,42,43,44,47,51,54,55,56,57,58,59,62,66,67,68,69,70,71,72,75,76,78,79,80,82,83,84,85,88,90,91,93,94,101,102,103,104,105] 
Network usage charge between the seller and the buyer  [52,101]  
Lower and upper limits of imported/exported energy and natural gas from/to utility companies  [19,32,35,39,56,65,83,90]  
Nonconvex branch flow model to model the distribution network  [87]  
Transmission limits (active and reactive power limits in electricity networks, maximum mass flow rate in gas and heating networks, power, gas, and thermal flow equations, single flow direction)  Boundaries of the active and reactive power in the transmission lines  [46,49,54] 
Gas flow equations in active and passive pipelines  [49,97]  
Single direction for the flow of energy in pipeline and pipeline capacity  [101]  
Nodal limitations at the EH level  Mass balances for each node  [22,86] 
Endusers constraints  [88,90]  
Active and reactive power balance equations in the hub  [27,106]  
Maximum and minimum nodal voltages, maximum and minimum gas pressure, maximum and minimum supply and return temperature  [19,30,49]  
Constraint on feedin power when grid security concerns  [92] 
Market Constraints  References 

Energy trading balance between crowdsources  [65] 
Prevent buying and selling electricity in the same time period  [36,57,85] 
Selling electricity byproducts on the spot market  [86] 
Estimation of the internal price  [81,92] 
Constraints related to mutual energy sharing  [19,41,50,91] 
Other Constraints  References 

Inequality constraints about the trading and the platform service charge  [52] 
Target the renewable penetration level  [49] 
εconstraint (parametric optimization method) and ICC  [104] 
Approach  References 

MILP  [19,20,21,22,23,26,27,28,30,32,33,34,35,39,40,41,43,44,45,47,50,54,56,57,58,59,62,63,66,67,69,72,74,75,79,81,82,86,88,89,90,93,95,101,105,111,112,113,114,115,117,118,119,120,121] 
MINLP  [23,25,29,36,46,49,70,76,78,80,96,97,98,102,106,107,108,109,110,122] 
MILP & MINLP  [24,65,83,84,88,92,123,124] 
Method  Description  References 

MOMFEAII  The multiobjective multifactorial evolutionary algorithm II (MOMFEAII) is a multitasking method where multiple multiobjective problems are optimized simultaneously. Each component of the multiobjective problems contributes to a unique factor affecting the evolution of a population of individuals. This algorithm uses the concept of nondominated rank (NR) and crowding distance (CD) in the nondominated sorting genetic algorithm (NSGAII) to define the fitness of each individual.  [94] 
Paretobased multiobjective evolutionary algorithms (MOEAs)  A Paretobased multiobjective solution uses evolutionary algorithms to find nondominated solutions on the Pareto front, which considers multiple objective functions at the same time as tradeoffs. This method guarantees good performance in numerous application areas. This algorithm is, in fact, easy to implement since it does not require detailed knowledge of the domain of the case under study.  [34,97,107,124] 
Modified teaching–learningbased optimization algorithm (MTLBO)  In the MTLBO algorithm, the methods of the teaching phase and learning phase are, respectively, modified to enhance the disturbance potential of search space, and a new “selflearning” method is presented to enhance the innovation ability of the learner and the global exploration performance.  [96,108,109] 
NSGAII algorithm  The NSGAII is employed to guarantee the feasibility and accuracy of the model solution. In this methodology, elitism and a maintenance methodology are used to increase diversity. A classification of the solutions according to an order of dominance is used. The assignment of a level or a front of dominance to all the solutions of one population is the basis of the NSGAII. This method is more appropriate for dealing with nonlinear problems, which are more complex to overcome with other multiobjective optimization methods.  [77,78,107,110,125] 
VIKOR  The multicriteria decisionmaking method, VlseKriterijumska Optimizacija I Kompromisno Resenj (Vikor), can be employed to select the optimal solution from Pareto solutions. This technique is specific to selecting alternatives with respect to conflicting criteria based on an aggregating function that measures the distance to the best solutions.  [78] 
Ɛconstraint  The Ɛconstraint method optimizes the main objective while other objectives are assumed as constraints of the problem. This approach is influenced by constraints choice; in addition, it can solve nonconvex optimization problems.  [14,46,58,72,79,85] 
Weightedsum method  A singleobjective function is formulated as a weighted sum of the objective functions. This method is employed to find the Pareto front, consisting of the best feasible tradeoffs between the objectives that can be discovered by varying the weight in the interval 0–1.  [39,40,41,42,43,44,54,66,86,93,96,98,101,106,107] 
Compromise programming method  The application of the compromise programming method aims to the modification of the decision model to include only one objective. The optimum solution can be identified as the one with the shortest distance to the optimum value.  [62,70] 
Heuristic Method  Characteristics  Examples 

Simple heuristics  Faster calculation of solution; Prone to get stuck in local optima.  Local search; Greedy algorithms; Hill climbers. 
Metaheuristics  Attempts to obtain a better solution in a predefined neighborhood; Many methods are based on biological metaphors.  Evolutionary algorithms; Genetic algorithms Simulated annealing; Particle swarm; Tabu search; Ant colony; Hybrid algorithms. 
Reference  Purpose  Method 

[16]  Operation and control of EH  Does not apply (literature review) 
[31]  Planning  Quantum particle swarm optimization (QPSO) 
[65]  P2P exchange  Alternating direction method of multipliers (ADMM) 
[67]  Scheduling of ESS  Fuzzy inference system 
[78]  Optimize electrothermal DR  Multistrategy gravitational search algorithm (MSGAII) 
[81]  P2P exchange  Step length control and learning process involvement 
[83]  Optimize operative costs  Modified bacterial foraging optimization (MBFO) 
[96]  Minimize costs and emissions  Fuzzy decisionmaking 
[99]  Energy markets  Does not apply (literature review) 
[103]  Scheduling of DER  Scenariobased branch and bound 
[106]  Environmental/economic dispatch  Particle swarm optimization (PSO) and differential evolution (DE) 
[107]  Planning  Does not apply (literature review) 
[108]  MOOPF  Modified teaching–learningbased optimization algorithm 
[109]  MOOPF  Fuzzy decisionmaking 
[110]  MOOPF  Multiagent genetic algorithm (MAGA) 
[124]  Planning  Does not apply (literature review) 
[125]  Optimize RES mix  Elitist benetic algorithm 
Optimization Solver (Algorithm Used)  Modeling Environment  References 

Not available *  MATLAB (Optimization toolbox)  [21,47,60,71,77,91,92,94,108,111] 
Gurobi (barrier and simplex algorithms)  MATLAB  [27,59,84,101] 
BMIBNB (branch and bound)  MATLAB (YALMIP toolbox)  [84] 
Not available *  MATLAB  [79] 
CPLEX (simplex and branch and bound algorithms)  MATLAB (YALMIP toolbox)  [50,56] 
Not available *  MATLAB + GAMS: MATLAB was used to develop the system operation model, and GAMS was used for the optimization phase  [34,125] 
CPLEX (simplex and branch and bound algorithms)  MATLAB  [19,28,72] 
CPLEX (simplex and branch and bound algorithms)  GAMS  [23,24,25,26,30,45,57,58,64,67,74,75,82,90,102,112,113] 
DICOPT (outerapproximations algorithm)  [29,32,36,51,56,78,80,88]  
BARON (branch and reduce algorithm)  [102]  
CPLEX (simplex and branch and bound algorithms)  IBM ILOG CPLEX  [33,39,40,41,42,43,44,54,55,81,93,114] 
Not available *  Xpress  [62] 
Not available *  LINGO  [70] 
Not available *  MATPOWER TOOL  [52,110] 
Not available *  CVXPY  [65] 
Gurobi (barrier and simplex algorithms)  Python + GAMS/SCENRED tool for reduction of scenarios  [38,69,85] 
Not available *  Python (RLLab)  [87] 
Uncertainties  References 

Renewable generation  [16,24,28,31,38,40,45,46,49,50,55,61,65,77,85,86,90,91,99,111,114,116,123] 
Consumption  [11,13,20,28,30,36,38,40,41,45,46,60,61,62,65,72,76,77,79,85,86,87,91,99,100,111,114,115] 
Storage and EVs  [19,31,33,67,123] 
Energy price  [11,13,19,38,39,40,46,52,55,60,61,64,76,85,86,87,99,100,105,114,115,126] 
Failure  [77] 
Thermal load  [127,128] 
Risks  Related Parameters 

Financial risks  Electrical loads; Thermal loads; Solar irradiation; Electricity prices. 
Reliability and power quality risks  Deviations of demands; PV power; Wind power; Electricity prices. 
Reference  DAM  IDM  Ancillary (Grid) Service  Gas/Fuel Trading  Demand Response  Other Markets or Resources 

[63,69,86,94,114]  Pricebased  Thermal needs  Biogas [94] Hydrogen [69] Solar thermal [63]  
[32,34,71,103]  Pricebased  Thermal needs  DR, TOU (all) Fast ramp [34]  Hydrogen [32,34]  
[28,31,33,35,39,40,44,50,72,74,79,82,83,84,85,88,104,125]  Pricebased  Pricebased [35,50,84]  Gas pricebased  Water [88] Biomass [39]  
[19,26,27,29,54,55,57,58,70,78,93,119,120]  Pricebased  Pricebased [27]  Gas pricebased  DR, TOU (all) V2G [55] Shedding [27]  
[25]  Network  DHN network  DR, TOU  
[30,49,96,97]  Network  Gas network  
[23]  Network  Gas network  Shedding  Hydrogen  
[36]  Network  Contingency  Gas network  
[116,123]  Network  Pricebased  Reserve  Gas network  
[66]  Pricebased  Pricebased  Grid fee  Gas pricebased  
[65]  Pricebased  Pricebased  System operator Control  
[131]  Pricebased  Reserve  
[129,130]  Pricebased  Pricebased  Reserve  Network and gas market    Green hydrogen and carbon markets 
Centralized Architecture  Decentralized Architecture  Distributed Architecture 

Provides direct setpoints to which the consumption must adjust to; Allows for a more straightforward network operation; Most intrusive method; Allows for a coordinated response; More complexity in the optimization algorithm. It can imply a very high computational burden.  No supervisory figure in the energy exchange; Minimal exchange of information; Not able to perform coordinated actions for external actors.  Allows a certain degree of influence on consumer patterns; Cannot establish a specific setpoint; Less demand for communication infrastructure; It requires the definition of a suitable pricing mechanism by the coordinator to manage the internal energy trading market; Slow convergence of the P2P market algorithm to reach an agreement about the energy transactions may occur. 
Reference  Business Model  Objective 

[19]  B2C, C2C  Reduce the total cost of the EH 
[46,105,108]  B2C  Minimize operational costs 
[48,57,87]  B2C, C2C  P2P exchange 
[52]  B2C  Maximize profits (P2P energy trading) 
[56]  B2C, C2C  Minimize the total energy expenditure 
[60]  C2C  P2P exchange 
[62]  B2C  Minimize costs and emissions 
[65]  B2C, C2C  Minimize electricity costs 
[67]  B2C, C2C  P2P exchange, minimize energy costs 
[70]  B2C, C2C  Minimize energy costs and emissions 
[75]  C2C  Minimize the daily operation cost 
[90]  B2B, B2C  Trade between EH, minimize costs and emissions 
[93]  B2C  Maximize the operator’s profit and reduce the CO_{2} emissions 
[97,109,110]  B2C  Minimize costs and emissions 
[99]  B2C, C2C  P2P energy sharing 
[106]  B2C, C2C  Reduction of the fuel cost and emission 
[111]  B2C  Reduction of the expected timeahead energy costs 
[124]  B2C  Minimize costs and emissions (literature review) 
[125]  B2C  Maximize RES output 
Temporal Resolution  References 

Half min  [87] 
One min  [35] 
Five min  [65,91] 
Fifteen min  [112,116,122] 
Half hour  [103] 
One hour  [19,20,21,23,25,26,28,29,30,36,38,40,42,43,46,47,49,50,54,55,57,60,61,63,67,68,72,74,75,77,79,80,83,90,92,94,98,102,104,105,113,115,123,125] 
Two hours  [101] 
Four hours  [86] 
Six periods per day  [85] 
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© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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Papadimitriou, C.; Di Somma, M.; Charalambous, C.; Caliano, M.; Palladino, V.; Cortés Borray, A.F.; GonzálezGarrido, A.; Ruiz, N.; Graditi, G. A Comprehensive Review of the Design and Operation Optimization of Energy Hubs and Their Interaction with the Markets and External Networks. Energies 2023, 16, 4018. https://doi.org/10.3390/en16104018
Papadimitriou C, Di Somma M, Charalambous C, Caliano M, Palladino V, Cortés Borray AF, GonzálezGarrido A, Ruiz N, Graditi G. A Comprehensive Review of the Design and Operation Optimization of Energy Hubs and Their Interaction with the Markets and External Networks. Energies. 2023; 16(10):4018. https://doi.org/10.3390/en16104018
Chicago/Turabian StylePapadimitriou, Christina, Marialaura Di Somma, Chrysanthos Charalambous, Martina Caliano, Valeria Palladino, Andrés Felipe Cortés Borray, Amaia GonzálezGarrido, Nerea Ruiz, and Giorgio Graditi. 2023. "A Comprehensive Review of the Design and Operation Optimization of Energy Hubs and Their Interaction with the Markets and External Networks" Energies 16, no. 10: 4018. https://doi.org/10.3390/en16104018