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;
- “End-user” sector coupling technologies: energy conversion technologies for the electrification of the end-uses that enable the flexibility of end-users/prosumers to be activated;
- “Cross-vector” 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, multi-objective 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 peer-to-peer (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 multi-objective approaches, multi-carrier 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 multi-faceted 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 state-of-the-art 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;
- Self-sufficiency 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. Multi-Objective Optimization Problems and Methods
- The preference-based approach in which the decision-making is performed before the optimization. This approach requires a good knowledge of the preferences of decision-makers 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 decision-making. This approach is more desirable than the previous one, as it is less subjective and leaves the final decision to the decision-makers.
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;
- Chance-constrained optimization introduces probabilistic constraints for obtaining a trade-off 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 multi-objective 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 multi-faceted 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 specific-country 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 | Business-to-business | MAGA | Multi-agent genetic algorithm |
B2C | Business-to-consumer | MBFO | Modified bacterial foraging optimization |
BS | Bill sharing | MECS | Multi-energy coupled systems |
C2C | Consumer-to-consumer | MES | Multi-energy system |
CAES | Compressed air energy storage | MGEM | Microgrid energy management |
CD | Crowding distance | MILP | Mixed-integer linear programming |
CES | Community energy storage | MINLP | Mixed-integer nonlinear programming |
CHP | Combined heat and power | MIQCP | Mixed-integer quadratically constrained |
DAM | Day-ahead electricity market | MISOCP | Mixed-integer second-order cone programming |
DDRO | Data-driven distributionally robust optimization | MMR | Mid-market rate |
DE | Differential evolution | MOEA | Multi-objective evolutionary algorithms |
DER | Distributed energy resource | MO-MFEA-II | Multi-objective multifactorial evolutionary algorithm II |
DHN | District heating network | MPC | Model predictive control |
DRO | Distributionally robust optimization | MSGA-II | Multi-strategy gravitational search algorithm |
DRP | Demand response programs | MTLBO | Modified teaching–learning-based optimization algorithm |
EA | Evolutionary algorithms | NR | Non-dominated rank |
EH | Energy hub | NSGA | Non-dominated sorting genetic algorithm |
EHGHS | Electricity–hydrogen–gas heat integrated energy system | nZED | Net-zero energy districts |
ESP | Energy-sharing 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 | Power-to-gas |
EU | European Union | P2P | Peer-to-peer |
EV | Electric vehicle | PCC | Point Of common coupling |
GA | Genetic algorithms | PCM | Phase change materials |
GfG | Gas-fired generation | PHEV | Plug-in hybrid electric vehicle |
GHG | Greenhouse gas | PRIMES | Price-induced narket equilibrium system |
GIL | Grid integration level | PSO | Particle swarm optimization |
HER | Heat-to-electricity ratio | PV | Photovoltaic |
HES | Hybrid energy system | QPSO | Quantum particle swarm optimization |
HP | Heat pump | RCGA | Real coded genetic algorithm |
HVAC | Heating, ventilation, and air-conditioning | 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 | Vehicle-to-grid |
ILECs | Integrated local energy communities | VIKOR | Vlsekriterijumska optimizacija i kompromisno resenj multi-criteria decision-making method |
KPI | Key performance indicator | VRE | Variable renewable energy |
LAES | Liquid air energy storage | WC | White certificates |
LEC | Local energy community | ZCMES | Zero-carbon multi-energy 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] | H2 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] | H2 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 | H2 storage | |||
[20] | Batteries | Thermal Storage | H2 storage | Natural gas storage | Thermal Storage | |
[18,23] | Batteries | H2 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 | H2 storage | |||
[58] | Batteries, CAES | Thermal storage | H2 storage | Thermal storage | ||
[19] | PHEV | Thermal storage | ||||
[76] | Flywheel, batteries, CAES | |||||
[42,86] | Thermal storage | |||||
[69] | Thermal storage | H2 storage | Thermal storage | |||
[70] | H2 storage | |||||
[87] | Natural gas storage | |||||
[88] | Thermal storage | |||||
[45] | V2G EVs |
Reference | Multi-Objective 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 | Single-Objective 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 energy-sharing 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 energy-sharing 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, ramp-rate 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] | |
Non-convex 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] |
End-users 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 feed-in 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 by-products 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] |
ε-constraint (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 |
---|---|---|
MO-MFEA-II | The multi-objective multifactorial evolutionary algorithm II (MO-MFEA-II) is a multitasking method where multiple multi-objective problems are optimized simultaneously. Each component of the multi-objective problems contributes to a unique factor affecting the evolution of a population of individuals. This algorithm uses the concept of non-dominated rank (NR) and crowding distance (CD) in the non-dominated sorting genetic algorithm (NSGA-II) to define the fitness of each individual. | [94] |
Pareto-based multi-objective evolutionary algorithms (MOEAs) | A Pareto-based multi-objective solution uses evolutionary algorithms to find non-dominated solutions on the Pareto front, which considers multiple objective functions at the same time as trade-offs. 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–learning-based 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 “self-learning” method is presented to enhance the innovation ability of the learner and the global exploration performance. | [96,108,109] |
NSGA-II algorithm | The NSGA-II 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 NSGA-II. This method is more appropriate for dealing with nonlinear problems, which are more complex to overcome with other multi-objective optimization methods. | [77,78,107,110,125] |
VIKOR | The multi-criteria decision-making 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] |
Ɛ-constraint | The Ɛ-constraint 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 non-convex optimization problems. | [14,46,58,72,79,85] |
Weighted-sum method | A single-objective 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 trade-offs 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. |
Meta-heuristics | Attempts to obtain a better solution in a pre-defined 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 electro-thermal DR | Multi-strategy gravitational search algorithm (MSGA-II) |
[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 decision-making |
[99] | Energy markets | Does not apply (literature review) |
[103] | Scheduling of DER | Scenario-based branch and bound |
[106] | Environmental/economic dispatch | Particle swarm optimization (PSO) and differential evolution (DE) |
[107] | Planning | Does not apply (literature review) |
[108] | MO-OPF | Modified teaching–learning-based optimization algorithm |
[109] | MO-OPF | Fuzzy decision-making |
[110] | MO-OPF | Multi-agent 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 (outer-approximations 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 * | X-press | [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] | Price-based | Thermal needs | Biogas [94] Hydrogen [69] Solar thermal [63] | |||
[32,34,71,103] | Price-based | 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] | Price-based | Price-based [35,50,84] | Gas price-based | Water [88] Biomass [39] | ||
[19,26,27,29,54,55,57,58,70,78,93,119,120] | Price-based | Price-based [27] | Gas price-based | 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 | Price-based | Reserve | Gas network | ||
[66] | Price-based | Price-based | Grid fee | Gas price-based | ||
[65] | Price-based | Price-based | System operator Control | |||
[131] | Price-based | Reserve | ||||
[129,130] | Price-based | Price-based | 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 CO2 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 time-ahead 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] |
Spatial Scope | References |
---|---|
Single building | [66,125] |
Single area | [72,74,75,97,104,105] |
Interaction between different energy systems inside a larger one | [36,46,47,85,96,101,113] |
Grid of interconnected nodes | [30,86] |
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Papadimitriou, C.; Di Somma, M.; Charalambous, C.; Caliano, M.; Palladino, V.; Cortés Borray, A.F.; González-Garrido, 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, González-Garrido 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 González-Garrido, 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