Uncertainty-Based Models for Optimal Management of Energy Hubs Considering Demand Response (original) (raw)
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Optimal Scheduling of Energy Hubs in the Presence of Uncertainty-A Review
Energy Hub is an appropriate framework for modeling and optimal scheduling of multi-energy systems (MES). Energy hub provides the possibility of integrated management of various inputs, converters, storage systems, and outputs of multiple energy carrier systems. However, the optimal management problem in the energy hub is affected by various technical, economic, social and environmental parameters. Many of these parameters are inherently ambiguous and uncertain. Fluctuating nature of renewable energy sources (RES), energy prices in competitive and deregulated markets, the behavior of consumers, inherent variations in the surrounding environment, simplifications and approximations in modeling, linguistic terms of experts, etc. are just a few examples of uncertainties in the optimal management problem of energy hub. Ignoring such uncertainties in the process of modeling and optimization of energy hub leads to unrealistic models and inaccurate results. On the other hand adding these uncertainties leads to increased complexity of modeling and optimization. Therefore, to achieve a realistic model of MES in the form of energy hubs, identifying appropriate methods to address these uncertainties is essential. This paper reviews the different methods for the consideration of uncertainty in optimal scheduling of energy hubs. In this paper, different methods of modeling and optimization of energy hub are reviewed and classified and their strengths and weaknesses are discussed. A classification and review of the various methods that offered in the most recent research of MES in the field of uncertainty modeling are done to identify efficient methods for using in energy hub models.
Optimal Operation of an Energy Hub in the Presence of Uncertainties
2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe), 2019
This paper presents an operation strategy of energy hubs in the presence of electrical, heating, and cooling demand as well as renewable power generation uncertainties. The proposed strategy can be used for optimal decision making of energy providers companies, as well as, other private participants of hub operators. The presence of electrical energy storage devise in the assumed energy hub can handle the fluctuations in the operating points raised by such uncertainties. In order to modeling of hourly demands and renewable power generation uncertainties a scenario generation model is adopted in this paper. The considered energy hub in this study follows a centralized framework and the energy hub operator is responsible for optimal operation of the hub assets based on the day-ahead scheduling. The simulation result illustrates that in the presence of electrical energy storage devices the optimal operation of hub assets can be attained.
Electric Power Systems Research, 2021
Energy hubs (EH) are known as multi-carrier systems that integrate multiple energy resources to enable greater flexibility in the energy provision. In this study, a multi-objective decision-making framework is proposed to determine the optimal scheduling of EHs. The proposed model considers the total cost of the EH, emissions, power losses, and average reserve of EH, simultaneously. These objectives are prioritized based on the EH preference that can be different for each EH. In this strategy, the cost of the EH has the highest priority and is considered as the main objective. The emission, system losses, and system reserve simultaneously have been considered as secondary objectives. According to the prioritization made among objectives, a lexicography optimization is performed in which cost minimization is considered in the first step, and the secondary objectives are evaluated in the second step of optimization. The intermittency nature of the electrical and thermal loads, renewable generation, and market prices are applied to the model by stochastic techniques. The proposed multiobjective model has been tested on the non-real benchmark system (standard IEEE 5-bus test system). The simulation results show that the proposed model improves the reserve capacity, emission, and system losses.
2021 IEEE International Conference on Environment and Electrical Engineering and 2021 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe), 2021
High-level integration of wind energy in power networks has raised the need for flexible units. Gas-fuel generators (GFG) with fast startup/shutdown and high ramp rate capability can provide the required flexibility in the operation of wind energy. However, the operation of GFGs can be affected by the limitations of the fuel transmission system. Demand response programs can decrease the effect of fuel transmission system restrictions in the operation of GFGs and as well as, increase the integration of wind energy into the electricity network. In this work, a scheduling model for electricity and gas networks considering demand response programs is presented. Uncertainties pertain to wind energy and demand response program are addressed in this model. Moreover, power to gas technology is used to prevent wind curtailment. This scheduling model is based on the information gap decision theory (IGDT) that can assess the level of risk pertains to uncertainties. The proposed framework has been simulated on two different networks to represent the effectiveness of the model.
IEEE Transactions on Power Systems, 2019
Multi-energy systems are flexible energy systems that can benefit from energy resources to supply different energy demands. Due to the capabilities of multi-energy systems in generating different energy carriers, these systems have been rapidly expanded in power systems. After restructuring in power system in recent years and appearance of competent energy markets, energy systems operated within such environments have been usually exposed to uncertainties of various parameters such as price, demand and etc. In this paper, a novel optimization framework based on hybrid scenario-based/interval/information gap decision theory (IGDT) method is developed to investigate optimal operation of smart energy hubs (S. E. Hubs) subject to economic priorities, technical constraints of distribution network and uncertainties. Considering energy hubs equipped to smart facilities, demand side management programs (DSMPs) including price response and load response services have been available to motivate electrical consumers to revise their consumption pattern in order to satisfy economic priorities of energy hubs. By using the results of employed hybrid uncertainty modeling approach, the operator of S. E. Hubs can decide either to take risk-averse or riskseeking strategy against the uncertainties. Uncertainty based integration of S. E. Hubs into distribution network is evaluated regarding the IEEE 33-bus test system and the results obtained from simulations are presented for comparison.
IEEE Access
The increasing demand for energy carriers has expanded the use of energy hubs that employ distributed demand response programs to improve power system reliability and efficiency. Moreover, the unstable behavior of renewable resources, as well as the indeterminate electrical and thermal demands, create major problems for energy hub operation. Inspired by this, this paper presents a day-ahead scheduling framework for energy hubs (EH) in energy and reserve markets considering two main objectives of economy and pollution emission. The studied energy hub consists of a novel hybrid energy storage facility based on a fuel cell, wind power, photovoltaic energy, and a particular fuel cell unit in the presence of elastic demand. This multi-component system participates in energy and reserve markets as a single entity to optimize energy hub operation. The proposed method also models the uncertainty of wind speed, photovoltaic irradiance, and load using the Mont-Carlo method. The energy hub risk level is analyzed using the conditional value at risk (CVaR) approach to increase the EH operation and efficiency. The proposed multi-objective energy hub model is solved using the MINLP method in General Algebraic Modeling System (GAMS) to minimize operation cost and pollution emission. Finally, to prove the effectiveness of adding a new E-fuel energy storage system and considering uncertainties on energy hub operation, the proposed method is compared with other reported models. INDEX TERMS Energy hub, demand response program, conditional value at risk, reserve market, fuel cell, E-fuel energy storage. NOMENCLATURE CHP combined heat and power CVaR conditional value at risk DG distributed generation DRP demand response program EH energy hub ESS energy storage system GAMS general algebraic modeling system GDRP gas demand response program PHESS pico hydel energy storage system The associate editor coordinating the review of this manuscript and approving it for publication was Fabio Mottola .
2019 International Conference on Smart Energy Systems and Technologies (SEST), 2019
In recent decades, the rising penetration of various types of distributed energy resources has made interactions between all types of energy inevitable. In this respect, energy hubs are created with the aim of considering the interactions between multi-carrier energy systems throughout the smart grids. In this research, optimal scheduling of the multi-energy hubs is considered in the day-ahead market with the aim of minimizing the energy hub's cost. Because of the high usage of the clean energy production potential by employing the wind turbines and PV panels at each energy hub, the proposed model will mitigate the greenhouse gas emissions through reducing the operation of the gas-fired systems over the scheduling horizon. The combined cooling/heating and power system is also used as a backup unit for the stochastic producers to ensure energy supply with minimum load shedding. Moreover, electrical and thermal energy storage devices are also employed for storing energy during time intervals when there is a large amount of clean and free energy production. The Monte-Carlo simulation approach is used for modeling the uncertain behaviors of the stochastic producers and fast forward selection method is also used for the scenario reduction process. The flexibility of the energy demand is also investigated using demand response programs. In order to validate the effectiveness of the proposed model, IEEE 10-bus standard test system integrated with distributed energy resources is used. Simulation results demonstrate the applicability and usefulness of the proposed model in the energy management of multi energy hubs.
The energy hub is defined as the multi-input multi-output energy converter. It usually consists of various converters like thermal generators, combined heat and power (CHP), renewable energies and energy storage devices. The plug-in electric vehicles as energy storage devices can bring various flexibilities to energy hub management problem. These flexibilities include emission reduction, cost reduction, controlling financial risks, mitigating volatility of power output in renewable energy resources, active demand side management and ancillary service provision. In this chapter a comprehensive risk hedging model for energy hub management is proposed. The focus is placed on minimizing both the energy procurement cost and financial risks in energy hub. For controlling the undesired effects of the uncertainties, the Information gap decision theory (IGDT) technique is used as the risk management tool. The proposed model is formulated as a mixed integer linear programming (MILP) problem and solved using General Algebraic Modeling System (GAMS). An illustrative example is analyzed to demonstrate the applicability of the proposed method.
Optimal Operation of Energy Hubs Considering Uncertainties and Different Time Resolutions
IEEE Transactions on Industry Applications, 2020
This paper presents a robust chance-constrained optimization framework for the optimal operation management of an energy hub in the presence of electrical, heating, and cooling demands and renewable power generation. The proposed strategy can be used for optimal decision making of operators of energy hubs (EHs) or energy providers. The electrical energy storage device in the studied energy hub can handle the fluctuations in operating points raised by such uncertainties. In order to model the hourly demands and renewable power generation uncertainties, a robust chanceconstrained close-to-real-time model is adopted in this paper. The considered energy hub in this study follows a centralized framework and the energy hub operator is responsible for the optimal operation of the hub assets based on the day-ahead scheduling. A thorough analysis of energy flows with different carriers is presented. In addition, a numerical stability test regarding the selection of the time step size is performed to guarantee the solution's time resolution independence, occurring in previous studies.