Smart Energy Hub Research Articles

Key performance indicators for the evaluation of building indoor air temperature control in a context of demand side management: An extensive analysis for Romania

Besides being the main consumer of energy, buildings can be smart energy hubs offering demand side flexibility for the grid. The main loads in buildings are heating and cooling and they have the advantage to be flexible if controlled in a smart way. There are plenty of ways in which controllers can act for increasing the demand side energy flexibility of buildings and different approaches in quantifying and assessing it.This study uses a set of three existing key performance indicators to make a qualitative assessment on how the heating and cooling energy control is impacting indoor thermal comfort, the costs with energy and the self-consumption of the locally produced energy for a typical household located in different geographical conditions in Romania, under an electricity market design with a demand response program. The selected metrics are adapted for this analysis to allow a quick qualitative evaluation on how the building and its systems can reach the maximum achievable potential in terms of comfort, costs, and self-consumption. This approach can be applied both at design and operational phase. The results show that the participation in a demand response program does not influence negatively the indoor thermal comfort if constraints are imposed on these parameters, that the geography does not have a relevant impact upon the costs if a control technique dedicated for the participation in the demand response program is implemented and the participation in a demand response program does not influence the self-consumption.

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

A new community based polygeneration smart energy system is proposed to meet the demands of a community in a sustainable manner and applied to case study where we aim to balance Ontario’s energy requirements while minimizing carbon emissions. In this case study, the smart energy hub utilizes a 30 MW small modular nuclear reactor which provides a unique and novel gas system at high temperature level integrated for the production and transportation of chemical energy; this occurs through a circuit in which an endothermic reaction (steam methane reforming plant) is connected to an exothermic reaction (methanation plant) and appears to be an attractive and preferable solution that meets the environmental requirements for a stable and efficient energy supply process. Complex multiscale subsystems are developed, modeled and simulated in the Aspen plus. Thermodynamic analyses on different sub-plants are presented, considering natural gas as the main feedstock, and incorporating power and heat production through chemical looping. The energy hub is capable of generating 10.8 MW of electricity and supplying 17.9 MW of heat for district heating. Hence, the overall energy and exergy efficiencies of the smart energy system are found to be 54% and 76% respectively. Also, a power to gas plant is integrated into the energy hub for the production of anhydrous ammonia with a capacity of 1816.22 tons/day and 100% carbon capture capabilities. Moreover, the impact of varying the input proportions of the system on the products and functionality of the energy hub are investigated.

Smart City as an Smart Energy Hub: A Bibliographic, Analytic and Structural Review

Smart City as an Smart Energy Hub: A Bibliographic, Analytic and Structural Review

A risk-based optimal self-scheduling of smart energy hub in the day-ahead and regulation markets

Utilizing multi-carrier energies such as wind energy, electric vehicle (EV) and battery banks are a significant step toward a cleaner production. Hence, This paper proposes a stochastic-based decision-making framework for the efficient short-term management of smart energy hub (EH) in restructured power systems with high penetration of renewable energy. The electrical and natural gas carriers are the input of smart EH, while the electricity and heat demands are considered as the outputs. The EH, including battery storage system (BSS) and EV fleet, is managed in the regulation market and day-ahead (DA) horizons. The energy hub operator makes optimal decisions regarding the natural gas network and energy supply for the thermal and electricity customers. An optimal self-scheduling model is developed to take into account the day ahead (DA) and regulation markets (RM) and decisions regarding generations of electrical and thermal devices as well as EV aggregator decisions. The primary goal of the proposed framework is minimizing the cost of procuring electricity and heat energy carriers in DA and regulation markets, including upward/downward regulations via a stochastic mixed-integer linear programming (MILP) approach. In order to get the uncertainty around the exact outcomes of RM prices, wind generation, and EV patterns, the model also takes into account the conditional value at risk (CVaR) term. The proposed formulation is examined by applying to a smart EH. Results show the effectiveness and usefulness of the proposed framework in managing the smart EHs efficiently.

Towards distributed based energy transaction in a clean smart island

Over the last few decades, the use of renewable and clean energy sources has been the core of attention of the researchers. Besides, utilization of the energy hubs as a great innovation for future multi-carrier energy systems is well perceived for integrating intermittent sources of energy into the energy systems, which is surged and bolded in recent years. In this regard, this paper proposes an effective distributed energy management framework for modeling and optimal operation management of clean smart islands based on primal-dual method of multipliers. The primal-dual method of multipliers approach has shown superior performance compared to the alternating method of multipliers for distributed optimization. In this study, two different agents including a smart energy hub and a microgrid comprises of renewable and clean energy sources are considered in the smart island. These two distinct energy systems are assumed to be equipped with communication apparatuses and are intended to negotiate over the energy they need in a proper and completely distributed manner. Results show the effectiveness, accuracy and applicability of the approach for energy communication.

Retracted: An Effective Energy Management Approach within a Smart Island Considering Water-Energy Hub

This paper proposes the novel concept and model of smart island (SI) in the power system. Over a few decades, the concept of smartness of power system has attracted many attentions due to the capability of interconnection of different systems within a smart platform. The proposed SI model is made up of different sections including the smart transportation systems (STS) consists of electric vehicles (EVs) and the subway, smart energy hub (EH) and a microgrid (MG). The coordinated supply of electricity, thermal and water demand within the SI's sections is also addressed in the proposed model. The islanding potential prepares a proper condition to supply the water demands of the SI's sections through the smart EH which is equipped with a desalination unit. Such a procedure can reduce the water demands' supplement costs at different sections, specifically the subway segments, by using the seawater. Moreover, the electrical demand of the subway is supplied by constructing the subway's charge stations (SCS) at the appropriate locations. In this model, the subway is able to increase its profit by having energy transactions with the EVs and MG through the proposed SCSs. Furthermore, the uncertain parameters associated to the SI are modelled using points estimate method (PEM). Results reveal the effectiveness and applicability of the proposed SI and optimization method.

An integrated two-level demand-side management game applied to smart energy hubs with storage

Abstract Energy hubs, an important component of future energy networks employing distributed demand-side management, can play a key role in enhancing the efficiency and reliability of power grids. In power grids, energy hub operators need to optimally schedule the consumption, conversion, and storage of available resources based on their own utility functions. In sufficiently large networks, scheduling an individual hub can affect the utility of the other energy hubs. In this paper, the interaction between energy hubs is modeled as a congestion game. Each energy hub operator (player) participates in a dynamic energy pricing market and tries to maximize his/her own payoff when satisfying energy demand. We propose a distributed algorithm based on a congestion game, which guarantees the existence of a Nash equilibrium. Furthermore, two different types of signaling are developed (price-based, load-based) and simulation results compared. Simulation results show that with the implementation of either setup the peak-to-average ratio between electricity networks and natural gas networks diminishes. An analysis of the results shows that either setup can dominate the other one with regard to generation costs, convergence rate, price level, and stability. Hence, energy providers and consumers can choose a favorable setup based on their respective needs.

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

Nowadays, to address the different demands of consumers, i.e., electricity, heat and gas, the concept of energy hub (EH) is emerged. Integrating multiple energy sources under the concept of energy hub can improve the efficiency and reliability of the system; moreover, efficiency increasing cause to decrease in environmental emission. Environmental emission is a very important issue from the global warming point of view that should be considered in the researches. In this regard, green hydrogen can be used as a clean energy carrier that can be stored for a long time without degradation. To this end, this paper focuses on the concept of hydrogen-based smart micro energy hub (SMEH) considering integrated demand response (IDR) and fuel cell-based hydrogen storage system (HSS). IDR is introduced to control consumers’ electrical and heat demand patterns. Additionally, HSS not only can convert power from renewable energy sources (RESs) to hydrogen (P2H) in a low electricity price period and vice versa (H2P) in a high electricity price period but also can supply the hydrogen-based industry. The aim of the proposed model is to minimize the total energy cost of hydrogen-based energy hub integrated with IDR and HSS under a robust optimization (RO) approach considering the uncertainty of electricity price. The proposed optimization model provides day-ahead scheduling for SMEH, which would improve the operation of the energy system. Finally, simulation and numerical results are provided to confirm the effectiveness of the proposed model. According to the obtained results, considered technologies cause to decrease in operation cost up to 7.8% and improve the system robustness against electricity market uncertainty up to 30%.

(Invited) Fuel Electrode Degradation for Solid Oxide Electrolysis Cells – How to Characterize It and What to Do about It

Solid oxide cells (SOC) are reversible electrochemical cells that can be operated as solid oxide fuel cells (SOFC) or as solid oxide electrolysis cells (SOEC). SOFC convert fuels such as H2 and natural gas into electric power and heat; while SOEC can be used e.g. for storing surplus renewable electrical energy via electrolysis of H2O and/or CO2 to produce fuels like H2, CO or synthesis gas (CO+H2). Synthesis gas can subsequently be catalyzed into a variety of synthetic fuels.In addition to the fact that SOC are made of abundant materials (no precious metals, expensive IrO2 or the like), the SOEC are also highlighted as superior when it comes to flexibility in comparison to well-known electrolysis technologies such as alkaline and PEM. The flexibility of the SOCs lies in: 1) reversible operation mode (rSOC) 2) high efficiency in both SOFC and SOEC mode, 3) operating at high current density with lower internal resistance than other electrolysis technologies and 4) high fuel flexibility e.g. the ability to operate the cell in CO2 electrolysis or co-electrolysis mode or with direct reforming of ammonia in SOFC mode. However; operating an SOC as rSOC is not necessarily simple and for the operation to match fluctuating supply from renewable energy sources, harsh rSOC operating conditions may be required [1,2]. Operating the SOEC at high current density is possible, but can lead to severe and irreversible degradation caused by migration of Ni in the innermost part of the fuel electrode as previously reported [3,4] and illustrated in Figure 1. Lastly, even though SOC offer large fuel flexibility, special attention should be paid to the effect of impurities in the gasses. For CO2 electrolysis operation significant degradation has been observed to be caused by traces (down to ppb level) of impurities. The effect becomes increasingly critical at operation conditions close to the carbon deposition threshold [5]. This talk will therefore touch upon the three issues: 1) Load cycling operation of SOC, 2) Ni/YSZ electrode degradation caused by Ni migration and 3) the puzzling interplay between impurities in the fuel and carbon deposition during CO2 electrolysis.In the context of load cycling operation of SOC results from a European project, REFLEX, will be presented. This project aims to develop an innovative renewable energy storage solution based on rSOC (“Smart Energy Hub”) for decentralized storage of electrical energy and to produce electrical energy and heat locally when needed. Tests in this project have included both single cells and stacks. Moreover; test operation schemes have been designed to investigate harsh load cycling operation.In relation to Ni migration; this talk will discuss and seek to answer questions such as: what is the critical parameter for onset of Ni migration away from the electrolyte/electrode interface and what is the driving mechanism for the transport? Can we use phase-field modelling to describe not only Ni coarsening but also Ni migration? Will Ni migration only occur for electrolysis when steam is present? Or will it also take place for CO2 electrolysis? And can we find means to minimize or fully hinder Ni migration?With respect to sensitivity towards impurities in the fuel and the interplay with carbon deposition, this talk will exemplify the effect of impurities – even in the ppb range - and seek to provide solutions for handling of gas stream impurities. Furthermore, this topic provides an excellent opportunity to showcase complementary characterization techniques, such as impedance spectroscopy, scanning electron microscopy (SEM) and Raman spectroscopy for investigation of the degrading Ni/YSZ fuel electrode.

Energy Management Considering Energy Storage and Demand Response for Smart Energy Hub in Internet of Things

This paper proposes an energy optimization management method of interaction between the smart energy hub (SEH) and users in the energy system. Internet of Things (IoT) and energy storage are also considered in the system. First, the models of the SEH and users are constructed. Then the profit models under the influence of energy storage are discussed. Next the Stackelberg game model is applied in this paper, where the SEH acts as the leader and the users are the followers. A double-layer algorithm is proposed to solve the problem. Finally, a case is studied to verify the correctness of the method. The results show that the method can be used for effective energy management in integrated energy system and IoT plays an important role in information exchange.

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

To exploit various energy carriers simultaneously and optimal distribution of energy in smart electrical infrastructure, a smart energy hub (S. E. Hub) concept was emerged. Therefore, the S. E. Hub is an effective solution for creating an efficient energy system. This paper attempts to provide a new management framework for the smart island which consists of Smart Energy and Water Hub (S. E. W. Hub) and microgrid. Also, optimal planning of multiple energy infrastructures of S. E. W. Hub is done considering operational constraints. Furthermore, the impact of the microgrid which includes wind turbines (WTs), photovoltaic power plant (PVPP) and tidal generation on the optimal planning of the S. E. W. Hub is investigated. The planning minimizes the total investment and operation costs as well as the environmental pollutants costs. In the proposed management framework, the balancing of thermal, electrical and water energy is provided. Due to the uncertainties associated with the considered energy sources, an effective scenario-based method is provided to accurately model such uncertainty factors. The validation of the proposed approach is shown through different cases implemented on a real industrial building and the problem is solved in the GAMS environment using CPLEX solver.

Optimal Scheduling and Management of a Smart City Within the Safe Framework

This paper proposes an enhanced cyber secure energy and data transaction framework for the optimal operation and management of the smart city. Recently, the concept of smart city within the power systems has taken the center stage. Although the power system full monitoring and accessibility is guaranteed through the smart city concept, the risk of cyber-attacks to the system has been promoted, severely. With this in mind, in this paper an effective smart city model including the smart grid, smart transportation systems (STSs) that consist of the metro and electric vehicles (EVs), microgrid and smart energy hub (EH) is presented. In the proposed model, an improved directed acyclic graph (DAG) approach is represented in order to enhance the security of data transaction within the smart city. In this approach, a security layer is added to the blockchain which will prevent the cyber hackers access to the system information. An effective energy management schedule is also developed in this paper. To do so, an intelligent priority selection (IPS) based on advanced math operators is provided to allocate the metro-owned charging stations (MCSs), optimally. Furthermore, the unscented transform (UT) is utilized to handle the uncertainty of the system parameters. The results showed that the proposed IPS could improve the method CPU time over 75% compared to other well-known meta-heuristic methods in the area. Moreover, the results showed that the proposed framework has remarkably reduced the run time and increased the accuracy of the solution compared to the other meta-heuristic algorithms.

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

Abstract In 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.

Intelligent Modeling and Optimization for Smart Energy Hub

This paper proposes an intelligent modeling and optimization method to set up the smart energy hub model for the multiple energy system and optimize the operation of its associated energy hub model automatically. The core idea of the proposed method is to divide the complex energy hub model into several simple energy hub models. In this paper, the proposed computerized algorithm, which is applied to the smart energy hub model division, includes an energy loop intelligent searching method, a real nodes arrangement method, and a virtual nodes insertion method. One case study for the multiple energy system of a residential district in Guangzhou city is performed to validate the effectiveness of the proposed method. Compared with the traditional calculation method, the proposed method guarantees global optimal operation decision for the whole multiple energy system and the computational burden is significantly reduced.

Energy scheduling for a three-level integrated energy system based on energy hub models: A hierarchical Stackelberg game approach

With the rapid development of information and energy generation technologies, the multi-level integrated energy system (IES) with multiple energy suppliers and end users has been vigorously promoted globally. In this study, the energy scheduling for a three-level IES is investigated by applying the hierarchical Stackelberg game approach. The IES is composed of one electricity utility company and one natural gas utility company (upper level), multiple same-structured smart energy hubs (S.E. hubs) that can produce electricity and heat simultaneously (middle level), and multiple users (lower level). By applying the Lagrange’s function, the operation strategies of all market participants are derived with analytical solutions, which are verified by a decentralized algorithm developed in this study. Simulation results show that the increase in the number of S.E. hubs decreases the energy prices received by users, increases the energy demands, and decreases the profit of each S.E. hub; therefore, each S.E. hub strives to crowd out other S.E. hubs as much as possible. Technological advancement is an effective strategy for S.E. hubs to rise above the market competition; therefore, S.E. hubs whose technological levels are lower than those of others are at an obvious disadvantage.

Tri-objective optimal scheduling of smart energy hub system with schedulable loads

Many researches have investigated the optimal energy scheduling to meet the demand regarding the sustainable development issues and the economic and environmental indices. The energy hub system (EHS) is introduced as an appropriate framework in which the demands are supplied by multi carrier energy (MCE) such as electricity, natural gas, and thermal energy. In this paper, the optimal scheduling problem of an EHS is modeled as a tri-objective optimization problem in which the operation cost, the emission pollution, and the deviation of the electrical load profile from its desired value is minimized. In the proposed model, the third objective function as a Demand-side Management (DSM) strategy is considered for the optimal shifting of the electrical deferrable loads (EDLs) based on the day-ahead electrical energy prices. Moreover, the reserve scheduling is proposed by the interruptible loads (ILs) of the thermal and the electrical demand. The proposed model is solved using the augmented ε-constraint method in the General Algebraic Modeling System (GAMS) optimization software environment. The generated solutions by the augmented ε-constraint method have diverse non-dominated solutions in which the best solution is selected by the fuzzy approach. In order to validate the proposed approach, three Case studies are investigated and their results are compared with each other. The operation cost and the emission pollution of the EHS in the first case is 402130.12 $ and 36162.26 kg. With the participation of EDLs in the second case, the operation cost and the emission reduces 0.4% and 3.02%, respectively. Finally, with the simultaneous participation of EDLs and ILs in the third case, the operation cost and the emission pollution reduces 0.38% and 2.53% in comparison with the second case. • Proposing tri-objective functions in smart EHS with optimal shifting of EDLs. • Implementation of optimal shifting and strategic conversion of DSM strategies. • Proposing ILs as reserve in smart EHS. • Utilization of augmented ɛ-constraint method to solve objective functions. • Finding best solution by fuzzy approach.

Multi‐energy Demand Response Management in Energy Internet: A Stackelberg Game Approach

A multi-energy Demand response management (DRM) approach in Energy Internet is proposed by employing Stackelberg game theory. The multi-energy trading problem in DRM is formulated as a Stackelberg game, where an energy provider, as a leader, adjusts energy prices, and residential smart energy hubs, as followers, dynamically schedule multiple energy flows according to the prices. The existence and uniqueness of the equilibrium of the proposed game model are analyzed. A multi-energy DRM algorithm for reaching the game equilibrium is developed. Simulation results show that the proposed game-based approach can effectively reduce residential energy costs and improve revenues of the energy provider.

Load Aggregator-Based Integrated Demand Response for Residential Smart Energy Hubs

In order to attract more flexible resource to take part in integrated demand response (IDR), this can be realized by introducing load aggregator-based framework. In this paper, based on residential smart energy hubs (S.E. Hubs), a two-level IDR framework is proposed, in which S.E. Hub operators play the role of load aggregators. The framework includes day-ahead bidding and real-time scheduling. In day-ahead bidding, S.E. Hub operators have to compete dispatching amount for maximal profit; hence, noncooperative game approach is formulated to describe the competition behavior among operators. In real-time scheduling, the dispatching model is formulated to minimize the error between real-time scheduling amount and bidding amount. Moreover, in order to reduce the influence of IDR on residential users, 4 categories of users’ flexible loads are modeled according to load consumption characteristic, and then these models are considered as the constraints in real-time scheduling. A case study is designed to validate the effectiveness of the proposed two-level IDR framework. And simulation results confirm that smart grid, S.E. Hub operators, and residential users can benefit simultaneously.

Risk‐constrained energy management of PV integrated smart energy hub in the presence of demand response program and compressed air energy storage

Multi-carrier energy systems create new challenges as well as opportunities in future energy systems. One of these challenges is the interaction among different energy hubs' facilities and various operational parameters on the scheduling of the energy hub systems. This paper deals with the problem of optimal scheduling of smart residential energy hub (SREH) considering the different uncertain parameters. The effect of the market prices, demands and solar radiation uncertainties on the SREH scheduling problem is characterised through a risk-constrained two-stage stochastic programming model. The objective of the proposed scheduling problem is to determine the least-cost 24 h operation of the facilities that would cover the cooling, thermal and electrical demands. The Monte Carlo simulation method is applied to model the inaccuracies of solar radiation, energy demands, and electricity market prices. Additionally, a proper scenario-reduction algorithm is employed to reduce the number of scenarios and simulation burden. The proposed approach evaluates the impacts of different values of risk aversion parameter and the utility of the demand response program on the optimal solution of the proposed PV integrated SREH scheduling. Finally, an illustrative example is provided to confirm the efficiency and the applicability of the proposed approach.

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

Demand-side management (DSM) and the integration of the energy hub concept as a main part of future energy networks play an essential role in the process of improving the efficiency and reliability of the power grids. In this paper, we consider a smart multi-carrier energy system in which users are equipped with energy storage and conversion devices (i.e., an energy hub). Users intend to reduce their energy payment by shifting energy consumption to off-peak hours and switching between different energy carriers. This system enables both users with shiftable loads and must-run loads to be active in a DSM program. We apply game theory to formulate the energy consumption and conversion for a distributed design. In this game, players employ their strategies to schedule energy consumption for appliances and conversion devices. We propose a novel algorithm which allows to solve this optimization problem which includes heat and electricity storage devices and multiple energy carriers for a system of interconnected hubs. The results show that using the proposed algorithm based on non-cooperative game formulation to connect a Smart Energy Hub (SEH) to a network can improve the performance of the whole network by reducing the peak-to-average ratio. Furthermore, users with and must-run loads can participate in this program by both switching between the electric and natural gas network and using storage devices.