An Integrated Energy Hub System based on Power-to-Gas and Compressed Air Energy Storage Technologies in presence of Multiple Shiftable Loads

Integrated energy carriers in the framework of energy hub system (EHS) have an undeniable role in reducing operating costs and increasing energy efficiency as well as the system's reliability. Nowadays, power-to-gas (P2G), as a novel technology, is a great choice to intensify the interdependency between electricity and natural gas networks. The proposed strategy of this study is divided into two parts: (i) a conditional value-at-risk-based stochastic model is presented to determine the optimal day-ahead scheduling of the EHS with the coordinated operating of P2G storage and tri-state compressed air energy storage (CAES) system. The main objective of the proposed strategy is to indicate the positive impact of P2G storage and tri-state CAES on lessening the system uncertainties including electricity market price, power generation of the wind turbine, and even electrical, gas, and thermal demands. (ii) A demand response program focusing on day-ahead load shifting is applied to the multiple electrical loads according to the load's activity schedule. The proposed strategy is successfully applied to an illustrative example and is solved by general algebraic modeling system software. The obtained results validate the proposed strategy by demonstrating the considerable diminution in the operating cost of the EHS by almost 4.5%

Techno-Economic and Environmental Assessment of Coordinated Operation of Regional Grid-Connected Energy Hubs Considering High Penetration of Wind Power

Nowadays, the high penetration of renewable energy sources (RESs) with non-uniformly distributed patterns has created unprecedented challenges for regional power systems to maintain system flexibility and reliability. These technical challenges obligate power system operators to curtail part of the produced renewable energy at various scheduling intervals. Motivated by these challenges, grid-connected energy hubs are seen as a way forward to boost system flexibility, decrease the rate of renewable power curtailment, and increase energy efficiency. However, simplified models may significantly affect the performance of the grid-connected energy hubs in practice. Hence, this paper proposes a holistic structure to determine the optimal coordinated operation of the grid-connected energy hubs and the regional power system by relying on the high penetration of wind power. In this regard, various fundamental challenges that have not yet been addressed in an integrated manner, including the CO2 emission rate and the amount of curtailed renewable energy along with total operating costs of the integrated energy system, are among the main objectives of the optimization problem. The proposed structure is developed in the form of tractable mixed-integer nonlinear programming (MINLP) problem to handle the day-ahead security-constrained unit commitment (SCUC). The information-gap decision theory (IGDT)-based robust model is used for accurate modeling of wind power uncertainty. The characteristics of the proposed IGDT-based robust SCUC model and its benefits are investigated through several technical case studies conducted on the modified 6-bus and 24-bus test systems. The simulation results validate the effectiveness and feasibility of the proposed structure. According to the obtained results for the 6-bus test system, networked energy hubs can help the system operator to reduce the total operating cost, wind power curtailment cost, and CO2 emission cost by 16.62%, 100%, and 30.44%, respectively, through utilizing up-to-date energy conversion facilities and energy storage systems as well as managing energy demands. It can be seen that the proposed strategy is a very effective step towards achieving a 100% renewable energy system

A Hybrid Robust Stochastic Approach to Evaluate the Profit of a Multi-Energy Retailer in Tri-Layer Energy Markets

Nowadays, multi-energy consumers in the industrial sector have a significant contribution in exchange of different forms of energy such as electricity, heat, and natural gas. So, multi-energy consumers can provide excellent opportunities for market players to trade power in various energy markets. In this paper, a new entity called multi-energy retailer is introduced to simultaneously meet both flexible and non-flexible electrical, gas, and heat demands of multi-energy consumers, with a high level of supply reliability. The multi-energy retailer is equipped with cogeneration facilities and various storage technologies such as power-to-x storages to exploit the actual arbitrage opportunities in different layers of energy markets. The presented structure successfully models the behavior of multi-energy retailer entity and seeks to maximize its profit as well as increase the welfare level of the multi-energy consumers. The uncertainties associated with electricity market price and various demands of multi-energy consumers can affect the profit and optimal day-ahead scheduling of the multi-energy retailer. In order to accurately model such uncertainties, a hybrid robust-stochastic approach is utilized in this study. This approach helps the multi-energy retailer’s operator to evaluate the worst-case of the scheduling process for the entity. Finally, the profit of the multi-energy retailer entity is estimated in the presence of conversion facilities, demand response programs, and various uncertainties based on actual energy market data

Optimal allocation of power-to-hydrogen units in regional power grids for green hydrogen trading: Opportunities and barriers

Due to the increasing hydrogen demands, a strong sense of commitment has recently been found to take advantage of the economic opportunities offered by power-to-hydrogen (P2H) units considering the high penetration of renewable energy sources (RESs). Deriving a market participation model for extracting green hydrogen with special attention to the grid code requirements is a fundamental challenge that has not yet been addressed. Motivated by this challenge, this paper presents a stochastic security-constrained optimal power flow (SSC-OPF) model to optimally allocate P2H units in renewable-dominated regional power grids. The main aim of the proposed planning model is to maximize the profit of power grid operators by extracting as much green hydrogen as possible and delivering it to the downstream industries. The presented model covers essential operational constraints, reserve adequacy issues, conservation voltage reduction, and uncertain behavior of demands and RESs to ensure the realistic operation of power grids. Moreover, the net present value of the proposed model is calculated to determine the profitability rate of using P2H units according to business models. The applicability of the proposed model is examined on the extended IEEE 30-bus and IEEE 118-bus test systems. The simulation results show that the use of P2H units in combination with RESs not only makes power grids more profitable but also improves the technical parameters. 2022 Elsevier LtdThis publication was made possible by Qatar University-NPRP11s1125-170027 from the Qatar University. The statements made herein are solely the responsibility of the authors.Scopus2-s2.0-8512957764

A centralized stochastic optimal dispatching strategy of networked multi-carrier microgrids considering transactive energy and integrated demand response: Application to water-energy nexus

Over a few decades, energy system operators have sought to achieve appropriate frameworks based on the water-energy nexus issues due to energy crises and the rapid growth of water demand. In this regard, multi-carrier microgrids (MCMGs) have been widely welcomed to implement water-energy nexus-related strategies to meet local energy and water demands. This paper presents a centralized stochastic optimization strategy for energy transactions in networked MCMGs to exploit the potential capabilities of the promoted energy conversion facilities in meeting electricity, thermal, and water demands at the lowest operating cost. To enhance the flexibility and operational cost of the system under severe uncertainties, the day-ahead scheduling of all individual MCMGs is carried out by a central operator with the consideration of transactive energy management (TEM) strategy and integrated demand response program (DRP). The MCMGs can purchase energy from the electricity and gas markets to supply demands and energize local generation resources, and also exchange electrical energy with each other under the TEM strategy. The uncertainties arising from the renewable power generation, energy demands, water demand, and electricity market prices are applied to the optimization model using a scenario-based method. The proposed strategy is formulated as the mixed-integer nonlinear programming problem and is solved under GAMS software. The effectiveness of the proposed strategy is validated using a test system consisting of three networked MCMGs. According to the obtained results, the central operator can reduce the total operating cost of the networked MCMGs considerably if employing the TEM strategy and integrated DRP. 2022 Elsevier LtdScopus2-s2.0-8513039714

Optimal Scheduling of Demand Response Aggregators in Industrial Parks Based on Load Disaggregation Algorithm

Nowadays, industrial parks play a significant role in the development of electricity market plans, and can thus provide excellent opportunities for market players to actively participate in various electricity markets. The demand response aggregator (DRA) is a major market player that can take advantage of these opportunities. In restructured electricity markets, identifying the consumption patterns of different classes of consumers can be effective in furthering the goals of the DRA. In previous studies on the self-scheduling of the DRA, consumer behavior has not been considered. Such an approach leads to numerous technical problems in the restructured electricity markets. For this purpose, herein, a practical mechanism is presented for executing the self-scheduling process of the DRA by considering the load disaggregation algorithm. The integration of self-scheduling and load disaggregation processes creates a hierarchical optimization problem. The main aim of the constructed hierarchical structure is to find the optimal self-scheduling of the DRA to consciously participate in the electricity markets by identifying the behavior of different consumers. The proposed structure is implemented and evaluated on the industrial park in Saveh, Iran. The time-of-use (TOU) and reward-based demand response (DR) programs are considered as the tools available for the DRA to trade the DR volumes in the day-ahead and balancing electricity markets. 2007-2012 IEEE.Scopus2-s2.0-8510720833