Generalized modeling and optimal management of energy hub based electricity, heat and cooling demands

Salehimaleh, Mohammad; Akbarimajd, Adel; Valipour, Khalil; Dejamkhooy, Abdolmajid

doi:10.1016/j.energy.2018.06.122

Cited by 72 publications

(16 citation statements)

References 20 publications

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“…The first approach historically contemplated has been to adopt DR capabilities, scheduling load according to the market needs and thus creating flexibility in the electrical network. DR is added to EH optimization through price signals sent by the energy supplier and the results of this optimization show gains for the consumer and assistance to the electricity grid, leading to a flatter demand curve [21]. Although this strategy already demonstrates important benefits for both industry and electricity market, it does not implement the recent advances in legislation which enables the creation of selfconsumption communities and prosumer microgrids.…”

Section: Industry 40 In the Energy Transitionmentioning

confidence: 99%

Future European energy markets and Industry 4.0 potential in energy transition towards decarbonization

Urbano¹,

Martinez-Viol²,

Kampouropoulos³

et al. 2024

RE&PQJ

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Climate change, economic growth and fossil fuel price volatility are forcing governments and thus society to adopt economical and technical measures in the energy sector to reach sustainability. These actions can be seen as opportunities for the stakeholders that form the energy market and also for new actors that may enter as a consequence of the energy transition that is taking place. In this paper, a description of the energy targets and potential market scenarios in Europe is carried out, together with a review of the policies implemented to achieve these objectives. Within this framework, the possibility of the industry to adopt a crucial role in the development of the new energy market is also analysed. The potential tools for its achievement are also presented, together with some of the techniques and mechanisms that make it feasible. From this study, it can be concluded that the industrial sector will become a major distributed prosumer, providing services to the energy market and facilitating the energy transition towards the decarbonization of the society.

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Section: Industry 40 In the Energy Transitionmentioning

confidence: 99%

Future European energy markets and Industry 4.0 potential in energy transition towards decarbonization

Urbano¹,

Martinez-Viol²,

Kampouropoulos³

et al. 2024

RE&PQJ

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“…In [24] cooperative trading framework of the SEHS as game theory approach (GTA), and with attention to stochastic characteristics of the RESs is proposed. In [25] optimal operation of the energy hybrid system in presence of the plug-in hybrid electric vehicles (PHEVs) uncertainty and risk-seeking constraints is investigated. The scheduling of the electrical SLs and PHEVs in the energy hybrid system in order to minimization of generation costs is studied in [26].…”

Section: Related Workmentioning

confidence: 99%

Multi-objectives Optimal Scheduling in Smart Energy Hub System with Electrical and Thermal Responsive Loads

Chamandoust

Derakhshan

Hakimi

et al. 2020

Environmental and Climate Technologies

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AbstractIn this study, multi-objective optimal scheduling of smart energy Hub system (SEHS) in the day ahead is proposed. A SEHS is comprising of interconnected energy hybrid system infrastructures such as electrical, thermal, wind, solar, natural gas and other fuels to supply many types of electrical and thermal loads in a two-way communication platform. All objectives in this paper, are minimized and consist of 1) operation cost and emission polluting in generation side, 2) loss of energy supply probability (LESP) in demand side, and 3) deviation of electrical and thermal loads with the optimal level of electrical and thermal profile in the day ahead. The third objective to flatten electrical and thermal demand profile using Demand Side Management (DSM) by the optimal shifting of electrical and thermal shiftable loads (SLs) is proposed. Also, stochastic modelling of renewable energy sources (RESs) and electrical and thermal loads by Monte Carlo technique is modelled. Using GAMS optimization software, proposed approach by ε -constraint method for obtaining to non-dominated Pareto solutions of objectives is implemented. Moreover, by the decision-making method, the best solution of non-dominated Pareto solutions is selected. Finally, two case studies and sensitivity analysis in case studies for confirmation of the proposed approach are analysed.

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“…Meanwhile electric, heating and cooling ESs (EESs, HESs, and CESs) increase the energy reallocation flexibility. EVs serve as mobile hubs connecting power grid and transport network due to the vehicle to grid technology [34], [39]; ALs and ILs provide friendly interactions depending on the energy prices, incentive payments, and regulation orders from EMS center.…”

Section: Typical Topologymentioning

confidence: 99%

“…Ref. [39] analyzed the cost-saving effects of ES, EV and controllable loads in SEH under time of use (TOU) program. Additionally, Ref.…”

mentioning

confidence: 99%

Resilient Configuration Approach of Integrated Community Energy System Considering Integrated Demand Response Under Uncertainty

Guo

Zhou

et al. 2019

IEEE Access

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Integrated community energy system (ICES) enables multi-energy synergy and impulses interaction of integrated demand response (IDR) from the demand side. Nevertheless, the randomness lies in IDR resources and renewable generation in the ICES configuration issue still lacks thorough analysis. Furthermore, the widely focused concern of resiliency urges more proactive consideration of potential emergencies at the planning stage of ICES. To this end, this paper proposed a resilience-oriented stochastic ICES configuration framework considering IDR influence. First, generalized IDR models are set up in detail with elaborate fuzzy feature analysis of price-responsive multi-energy loads. Then, the vulnerability indicators of tie lines and converting devices of the ICES are first introduced to demonstrate the occasional outage in the normal operation and blackout in the emergent case. In addition, the worst-case conditional value-at-risk (WCVaR) theory is innovatively integrated to the traditional risk-neutral model, which is formulated as a two-stage stochastic chance-constrained programming problem, aiming to combine portfolio with minimizing the worst-case cost caused by a disaster. The models are then transformed into mixedinteger linear programming problems via several linearization techniques. Finally, the results of the case studies showed the established model's superiority in improving the overall economy (reduce 6.94% of the worst-case cost) and system resiliency (decrease the value-at-risk by 3.20%) against an unforeseen hurricane. Meanwhile, the ICES reliability was also improved by declining 25.65% of unintended load curtailment faced with an emergency. The proposed approach provided an efficient preventive portfolio scheme of the IDR-integrated ICES for decision-makers' different risk preferences of natural hazards. INDEX TERMS Integrated community energy system, integrated demand response, vulnerability indicator, WCVaR theory, two-stage stochastic chance-constrained programming. NOMENCLATURE A. ACRONYMS ICES Integrated community energy system IDR Integrated demand response SEH Smart energy hub WCVaR Worst-case conditional value-at-risk TSCCP Two-stage stochastic chance-constrained The associate editor coordinating the review of this manuscript and approving it for publication was Salvatore Favuzza.

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Generalized modeling and optimal management of energy hub based electricity, heat and cooling demands

Cited by 72 publications

References 20 publications

Future European energy markets and Industry 4.0 potential in energy transition towards decarbonization

Future European energy markets and Industry 4.0 potential in energy transition towards decarbonization

Multi-objectives Optimal Scheduling in Smart Energy Hub System with Electrical and Thermal Responsive Loads

Resilient Configuration Approach of Integrated Community Energy System Considering Integrated Demand Response Under Uncertainty

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