A Portfolio Selection Model for Planning Natural Gas Smart Energy Hubs with a Multicriteria Benefit-to-Cost Ratio-Based Approach

The proliferation of technologies such as combine heat and power systems has accelerated the integration of energy resources in energy hubs. Besides, the advances in smart grid technologies motivate the electricity utility companies toward developing demand response (DR) programs to influence the electricity usage behavior of the customers. In this paper, we modify the conventional DR programs in smart grid to develop an integrated DR (IDR) program for multiple energy carriers fed into an energy hub in smart grid, namely a smart energy (S. E.) hub. In our model, the IDR program is formulated for the electricity and natural gas networks. The interaction among the S. E. hubs is modeled as an ordinal potential game with unique Nash equilibrium. Besides, a distributed algorithm is developed to determine the equilibrium. Simulation results show that in addition to load shifting, the customers in the S. E. hubs can participate in the IDR program by switching the energy resources (e.g., from the electricity to the natural gas) during the peak hours. Moreover, the IDR program can increase the S. E. hubs' daily payoff and the utility companies' daily profit.

A scenario-based approach for the design of Smart Energy and Water Hub

Management and real-time monitoring of interconnected energy hubs using digital twin: Machine learning based approach

Development and analysis of a polygenerational smart energy hub for sustainable communities

This paper proposes a portfolio selection model from the perspective of probabilistic hesitant financial data (PHFD). PHFD can be interpreted as the new form of information presentation that is obtained by transforming real financial data into probabilistic hesitant fuzzy elements. Based on the above data and model, we can derive the optimal investment ratios and give suggestions for investors. Specifically, this paper first develops a transformation algorithm to transform the general share returns into PHFD. The transformed data can directly show all the returns and their occurrence probabilities. Then, the portfolio selection and risk portfolio selection models based on PHFD, namely the probabilistic hesitant portfolio selection (PHPS) model and the risk probabilistic hesitant portfolio selection (RPHPS) model, are proposed. Furthermore, the investment decision-making methods are provided to show their practical application in financial markets. It is pointed out that the PHPS model for general investors is constructed based on the maximum-score or minimum-deviation principles to get the optimal investment ratios, and the RPHPS model provides the optimal investment ratios for three types of risk investors with the aim of obtaining the maximum return or taking the minimum risk. Finally, an empirical study based on the real data of China’s stock markets is shown in detail. The results verify the effectiveness and practicability of the proposed methods.

A cloud computing framework on demand side management game in smart energy hubs

An autonomous demand response program for electricity and natural gas networks in smart energy hubs

Optimal management of energy hubs and smart energy hubs – A review

The use of information and communication technology (ICT) and control systems in power systems has led to the creation of a concept called the smart grid. The development of this concept in power networks leads to optimal network control, optimal use of equipment, increased quality and reliability of power supply, facilitation of the integration of renewable energy sources (RES), optimal planning of the transmission and distribution systems, the development of the use of distributed generation (DG) and reduced system’s costs. However, in the past years, this concept has only been developed on the power grid and does not provide an accurate understanding of real energy systems. In real energy systems, different energy carriers and technologies interact, and a real energy system is a collection of these carriers and technologies. Therefore, the models presented for future sustainable energy systems should consider the integration of different energy infrastructure and the interaction of different energy carriers. In this regard, the concept of energy hub, in which the production, conversion, storage, and consumption of different energy carriers are considered in an intelligent framework, can provide a comprehensive model of future smart energy systems (SES). The main purpose of this chapter is to introduce the concept of smart energy hub (SEH). In this regard, an introduction to the concept of the smart grid, its definitions, features, and main challenges are presented. Finally, it discusses the framework of SEHs and their potential role in achieving a comprehensive model of SES in the future.

An interactive framework for strategic participation of a price-maker energy hub in the local gas and power markets based on the MPEC method

Robust scheduling of hydrogen based smart micro energy hub with integrated demand response

The combination of energy hubs with advanced information and communication technology has resulted in the creation of an intelligent system referred to as a smart energy hub (SEH). The implementation of the SEH has facilitated the enhancement of the entire energy distribution system by enabling a two-way exchange of energy and information between utility providers and consumers. This has resulted in a system that is secure, efficient, and dependable. The significance and visibility of big data in the SEH are evident as a result of the growing accumulation of data quantities. A wide range of equipment and software work together to collect and use energy data. This includes tools used by both energy providers and customers, like smart meters, software for billing, and various monitoring and control systems. Additionally, sensors, computers, and communication networks play a crucial role in collecting and transmitting this data across the energy grid. Hence, big data plays a crucial role in the development of an enhanced SEH. This paper presents an introduction to the notion of SEH and its associated concepts, as well as the function of big data in the context of SEH. It also discusses the obstacles that big data encounters in the SEH domain and explores the potential opportunities that big data offers for SEH.

Two-Level Distributed Demand-Side Management Using the Smart Energy Hub Concept

Network-augmented time-varying parametric portfolio selection: Evidence from the Chinese stock market

According to the necessity of establishing an effective, dependable, and cost-effective system to supply various demands, the significance of an energy hub has emerged in recent years. Also, the other aspect dealing with decreasing the global warming issues should be taken into account. To this end, a smart multicarrier energy hub (SMEH) coordinated with an integrated demand response program (IDRP) and hydrogen storage system (HSS) as flexible resources has been proposed. Managing the consumption of electrical and thermal demands is handled by IDRP, whilst fuel cell–based HSS technology has an important role in lessening the air pollutant emissions. In reality, HSS could convert electrical power to hydrogen energy via power to hydrogen (P2H) at low price hours or in high renewable energy resources (RERs) penetrations; however, at high price periods or in low RERs penetrations, electricity is generated through hydrogen-based fuel cells. In addition to the aforementioned parts, combined heat and power (CHP), boiler, gas-fired units (GFUs), HSS, electrical heat pump (EHP), energy and thermal storage systems (ESS/TSS), and RERs are also considered in the SMEH. The scenario-based stochastic method is utilized to handle the fluctuations of electricity price and RERs' output. The multiobjective scheduling framework of the problem is modeled as a mixed-integer linear programming (MILP) to investigate and obtain the economic–environmental benefits. Consequently, the applicability of the introduced methodology is verified via sample case studies. Simulation outcomes represent that applying IDRP and HSS results in a decrement in overall cost and total emission by about 1.54% and 2.9%, respectively.