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In this article, the transactive energy as a sustainable technology is introduced for the integration of numerous distributed energy resources (DERs) in a reliable manner. DERs can actively participate in the day-ahead (DA), real-time balancing (RTB), and wholesale gas markets for achieving the various goals. This article proposes a distributionally robust chance-constrained (DRCC) model based on the transactive energy approach for the optimal scheduling of microgrids in coupled electrical and gas networks. Optimal scheduling of DERs has been carried out for maximizing the microgrids’ profits in the DA electricity and gas markets while minimizing the imbalance costs is an objective for microgrids in the RTB market. Transactive energy technology is used for managing energy exchange between microgrids and the power grid. Moreover, the linearization techniques are employed for avoiding nonlinear equations to obtain reliable results in a short time. Due to the effects of the electrical and gas networks on each other, the interactions between them are considered by investigating both the electrical and gas energy conversions in the real system. Simulation results are extracted considering two cases: Case I without DRCC and Case II with DRCC. Given the results, the cost of interrupted load, DERs, and gas sector in Case II are, respectively, reduced to 35.4%, 25.02%, and 17.1% in comparison with Case I. Moreover, the DRCC method guarantees the achievement of $169027.759 profit for microgrids, which is reduced to 47.85% in comparison with the base case.

In recent years, the destruction of the ecosystem, due to the strong emission of greenhouse gas into the atmosphere by humans has led to significant damage to wildlife, human health, and flora. This has significantly changed the human life cycle. Nowadays, the environmental damage costs are applied for the countries strongly impacting pollutant emission. To this end, developing countries receive annual compensation from the countries considered to be the most polluting. So far, decision-makers seem to be unaware that, at the scale of an eco-neighborhood, some emerging countries produce also a significant amount of CO2. The main purpose of this research is to quantify and to compare the effect of the energy mix of 150 countries on three environmental impacts generated by an eco-neighborhood: greenhouse effect, energy demand, and biodiversity damage. To perform this comparison, the same neighborhood design is applied to 150 countries, but four parameters are adapted to each country: energy mix, local climate, building materials, and occupants’ mobility. In addition, this research evaluates the induced environmental costs of the neighborhood over a life cycle of 100 years and examines the impact of mobility and photovoltaic panel on these environmental costs. The different environmental impacts were evaluated by the Pleiades ACV simulation software under four phases (construction, use, renovation, and demolition), before being translated into environmental costs. Among the four local parameters (energy mix, local materials, climate, and transport), the energy mix has the most significant effect on the three studied environmental impacts. The results show that the countries having a higher concentration of renewable energy sources produce lower CO2 than others. Domestic and material wastes are also one of the main sources of greenhouse gas emissions and biodiversity damage in a sustainable neighborhood. The biodiversity damage is high in Sub-Sahara Africa, and MiddleEast, but low in the USA, Brazil, European Union, Russia, and Australia. The implementation of photovoltaic panels in a sustainable neighborhood mitigates, on average, 15.9% of carbon dioxide emissions and 21.2% of primary energy demand; but, unfortunately, this solution increases up to 25.0% the biodiversity damage.

At present, the power system is developing toward a fully renewable energy resources (RERs) equipped system due to significant challenges with the conventional units. This trend has led to remarkable new challenges for power system planners considering the stochastic nature of RERs, application of new emerging technologies, etc. In this article, a two-stage robust stochastic programming model for the optimal scheduling of commercial microgrids equipped with 100% RERs to handle the existing uncertainties is presented. In the day-ahead electricity market, microgrids maximize their expected profits by optimizing their bidding strategy, while minimizing the imbalance cost is targeted for microgrids by adjusting the distributed energy resources in the real-time balancing market. Transactive energy technology is effectively applied to manage the energy trading between microgrids with each other in the local energy transaction market and with the power grid. For demand-side management, the demand response program is posed considering the shiftable and interruptible features of the load. Simulation results on the IEEE 33-bus standard system integrated with microgrids verify that the proposed model could provide satisfactory profits for microgrids participated in the energy exchanging process based on the transactive energy architecture.

The retrofit of buildings to improve energy efficiency represents a vast opportunity in the construction market with the potential to reduce energy demand, create jobs, and achieve environmental benefits. Multiple challenges need to be overcome to unleash this potential and realize the benefits of investments in building energy retrofit projects. Small-/medium-sized commercial buildings are typically underserved by the energy retrofit industry due to the lack of owners’ ability to manage and finance energy efficiency improvements. In the case of energy-intensive and low-margin types of commercial buildings such as restaurants, the potential for energy savings is high, but the favorable conditions that enable retrofits, such as a pending renovation, propensity of clients to conserve energy, and willingness to take time for a retrofit, are scarce. In this research, the development of small energy efficiency projects is examined by proposing a screening method to find prospective candidates for financed energy efficiency projects in a targeted region. This method enables the co-creators of energy retrofit projects to predict the decision-making behavior of business owners and their willingness to invest in energy retrofit based on their restaurants’ attributes in social media. As part of a current initiative, supported by the Pennsylvania Department of Environmental Protection, a series of design, development, and delivery of retrofit projects in the context of food services was closely studied. The initial focus was on the Philadelphia region as a model for broader statewide applications. Restaurant owners were surveyed, and information was collected about their buildings’ energy systems, utility bills, and their willingness and/or capacity to invest in energy efficiency upgrades. A “Philadelphia restaurants” dataset was then scraped from the Yelp website. The data includes information about restaurant ratings, number of reviewers, price range, working hours, and ambiance. A methodology was developed to analyze the data and to find patterns and criteria that could possibly influence the owner’s decision for investing in energy performance improvement. To identify patterns, two methods were employed: (1) several focus groups over program period with team members who played roles in project development phase and (2) statistical analysis to identify the factors contributing to successful projects. Using these screening questions from focus groups and the results from statistical analysis, the retrofit likelihood can be estimated for each individual project. This methodology can be applied to larger datasets and would enable the investigators to screen the whole market and prioritize the targets for business recruitment.

In recent years, the advent of microgrids with numerous renewable energy sources has created some fundamental challenges in the control, coordination, and management of energy trading between microgrids and the power grid. To respond to these challenges, some techniques such as the transactive energy (TE) technology are proposed to control energy sharing. Therefore, this paper uses TE technology for energy exchange control among the microgrids, and applies three operation cases for analyzing the energy trading control of four and ten microgrids with the aim of minimizing the energy cost of each microgrid, respectively. In this regard, Monte Carlo simulation and fast forward selection (FFS) methods are respectively exerted for scenario generation and reduction in uncertainty modeling process. The first case is assumed that all microgrids can only receive energy from the network and do not have any connection with each other. In order to maximize the energy cost saving of each microgrid, the second case is proposed to provide a positive percentage of cost saving for microgrids. All microgrids can also trade energy with each other to get the most benefit by reducing the dependency on the main grid. The third case is similar to the second case, but its target is to indicate the scalability of the models based on the proposed TE technology by considering ten commercial microgrids. Finally, the simulation results indicate that microgrids can achieve the positive amount of cost saving in the second and third cases. In addition, the total energy cost of microgrids has been reduced in comparison with the first case.

Recently, the decarbonization of the electric power system has led to substantial efforts for designing a pathway toward 100% renewable energy resources (RERs). In this article, we propose a novel operational model for the effective participation of the interconnected microgrids with 100% RERs in the transactive energy market. The novelty of the proposed model is mostly related to the use of transactive energy technology for developing the free energy trading environment for the microgrids with 100% RERs as the local energy-trading market to establish a dynamic energy balance in the system. To capture the intermittencies in the system, a hybrid version of the stochastic programming and information gap decision theory (IGDT) method with the risk-averse and risk-seeker strategies is proposed in the deregulated environment. The proposed model is validated by selecting the modified IEEE 14-bus test system. The results indicate the effectiveness of the proposed model in providing the same percentage of cost-saving for microgrids when they simultaneously participate in the transaction energy market. The cooperative energy interactions of the microgrids in the transactive energy market based on the proposed model lead to 18.34% cost-saving for them in comparison with the base model.

Multiple numbers of Building Energy Simulation (BES) programs have been improved and implemented during the last decades. BES models play a crucial role in understanding building energy demands and accelerating the malfunction diagnosis. However, due to the very high number of interacting parameters, most of the developed energy simulation programs do not accurately predict building energy performance under a known condition. Even the energy models which are developed with the very precise assignment of parameters, there is always significant discrepancies between the simulation results and the real-time data measurements. Current study develops an optimization-based framework to calibrate the whole building energy model. The optimization algorithm attempts to set the identified parameters to minimize the error between the simulation results and the real-time measurements. Due to the high number of parameters, the developed optimization algorithm utilizes a Harmony Search algorithm as its search engine coupled with the energy simulation model to accelerate the calibration process. Moreover, to illustrate the efficiency of using the developed framework, a case study of the office building is modeled and calibrated and the statistical analysis was conducted to assess the accuracy of the results. The results of the calibration process show the reliability of the framework. - 2019 This work was supported by the Qatar National Research Foundation (QNRF)/National Priorities Research Program (NPRP 7 - 143 - 2 - 070). Authors would like to acknowledge their supports through this research. Scopus

Combined heat and power (CHP) plants have the capability of supplying power and heat energy altogether. The major aim of CHP economic dispatch (CHPED) is determining optimal generation management of cogeneration plants with a minimum operational cost of providing power and heat load demands. Several electrical and operational constraints should be studied in solving the CHPED problem, which includes valve-point influences of traditional thermal production units, the production capacity of cogeneration units and load balance. In addition, heat and power generated of CHP plants have a bidirectional association, which is known as a feasible operating zone, that makes the CHPED problem more complex. In this study, a novel multi-player harmony search (MPHS) method is introduced for solving the non-convex non-linear large-scale CHPED challenge in the power system. The presented MPHS algorithm in this study is based on utilizing experiences of multi-players for harmony improvisation during the optimization process of the problem. The presented MPHS algorithm is employed on a 24-unit test system and an 84-unit case study as a large-scale system to evaluate the efficiency of the presented MPHS method. The large-scale 84-unit system includes 40 thermal plants, 24 CHP units, and 20 boilers. The determined optimal solutions are compared with the literature to confirm the performance of the presented MPHS algorithm.

This study presents a bi-level model for expansion planning of generation systems. The proposed model endogenously determines the incentive to invest (guaranteed purchase contract) for wind and storage units. In the upper level of the proposed model, wind turbines and compressed air storage systems focus on maximising their profits by presenting a strategic offering, while at the lower level, there is an independent system operator with the purpose of increasing social welfare run the market clearing equations. In solving the bi-level model, the Karush-Kuhn-Tucker conditions are used to develop the mathematical program with equilibrium constraints. The problem was first linearised and then solved by GAMS software. The outputs of this problem are the installation time and capacity of the wind and storage units and the price of the purchase contract. The obtained results were compared with other existing studies for verification purposes.

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.

Hydrothermal systems play a significant role in electric energy systems as important power generation units, which have been studied in previous researches considering remarkable efforts. The optimal short-term hydrothermal scheduling (STHS) aims to attain optimal production scheduling of thermal and hydro plants for determining minimum operation cost of providing demand in the determined time interval. Different constraints should be studied in the solution of such issue containing limitations associated with water discharge, water storage, power production of plants, power balance of the system and water balance of hydro plants. In addition, valve impacts of thermal plants and complex hydraulic coupling of hydro plants are the other operational and technical constraints. In this study, the robust stochastic STHS is studied considering market price and demand uncertainties. Accordingly, robust optimization method is employed in this study to model price uncertainties. In addition, the uncertainties of load demand are handled using scenario-based modeling procedure. The scheduling problem of hydrothermal system is studied in a deregulated environment, where the hydrothermal system belongs to the private company and is capable to sell the surplus generated power to the market. Accordingly, the company aims to obtain maximum profit by selling power to market in addition to supplying power demand of the company. The introduced scheme for stochastic robust STHS is simulated and the provided solutions are investigated to verify the effectiveness of the scheme.

Recent advancements in energy systems have led to a series of new challenges in the decision-making process. Harmony search (HS) algorithm, which is a music-inspired optimisation technique, has bee...

This paper presents an optimization approach based on mixed-integer programming (MIP) to maximize the profit of the Microgrid (MG) while minimizing the risk in profit (RIP) in the presence of demand response program (DRP). RIP is defined as the risk of gaining less profit from the desired profit values. The uncertainties associated with the RESs and loads are modeled using normal, Beta, and Weibull distribution functions. The simulation studies are performed in GAMS and MATLAB for 5 random days of a year. Although DRP increases the total profit of the MG, it can also increase the risk. The simulation results show that RIP is reduced when downside risk constraint (DRC) is considered along with DRP implementation. Considering DRC significantly reduces the percentage of the risk while slightly decreasinz the profit.

Various energy networks such as electricity, natural gas, and district heating can be connected by emerging technologies for efficient application of renewable energy sources. On the other hand, the pressure shortage in the natural gas network and increasing heat loss in the district heating network by growth of gas and heat load in winter might play a significant role in the participation of combined heat and power units in the energy markets and operation cost of the whole integrated energy system. Hence, this paper presents a multi-network constrained unit commitment problem in the presence of multi-carrier energy storage technologies aiming to minimize the operation cost of an integrated electricity, gas and district heating system while satisfying the constraints of all three networks. In addition, an information gap decision theory is developed for studying the uncertainty of energy sources under risk-seeker and risk-averse strategies with no need for probability distribution function. Moreover, the role of multi-carrier energy storage technologies in integrated networks is investigated, which indicates decrement of total operation cost and reduction of the effect of wind power uncertainty on total operation cost in presence of the storage technologies.

Integration of renewable energy sources in electrical energy networks, is significantly increased due to economic and environmental issues in recent years and has appeared new challenges in the operation of power systems. Additionally, the power to gas (P2G) technology is a practical solution for accommodating the variability of the power output of wind energy sources, which are effective in reducing pollutant gas emissions considering their gas or power injection to the network at on-peak time intervals. Moreover, natural gas (NG)-fired generation plants can be introduced as practical solutions for decreasing power output variations of renewable sources due to their high ramp rates and quick response. This study proposes a multi-objective two-stage stochastic unit commitment scheme for integrated gas and electricity networks taking into account novel flexible energy sources such as P2G technology and demand response (DR) programs as well as high penetration of wind turbines. In this paper, P2G technology is introduced as a promising option for increasing the wind power dispatch in power systems. In addition, DR program as a cost-environmental effective method is modeled as a price-responsive bidding mechanism that is influential in decreasing the operation cost of the integrated network by shifting load from on-peak time intervals to off-peak time intervals. The introduced scheme has been implemented on an integrated 6-bus power system with 6-node gas networks by analyzing the performance of the framework in terms of operation cost and release of environmental pollutant gases. The results show that the simultaneous consideration of power-to-gas technology and demand response program reduces environmental pollution in addition to reducing costs. The investigation of the operation cost of the whole integrated system shows that application of both P2G and DR is beneficial in decreasing cost by 2.42% and 1.78% with respect to consideration of each of the P2G and DR, respectively.

The increasing costs of energy and water, fossil fuel depletion, and food shortages caused by climate change challenge long-term sustainability of food, energy, and water (FEW) systems. In working toward sustainable development, a fundamental question for deciding on whether and how to invest in FEW systems is “how sustainable FEW systems are?”. In order to measure sustainability across the FEW systems, an integrated sustainability index (SI) is developed. The SI is comprised of three components; food, energy, and water. These components each consist of different sub-components (e.g. transportation fuel for energy component) that make up integrated FEW systems. The sustainability of an FEW system can be calculated using the integrated FEW SI, but a more thought provoking question is to understand how each sub-component affects overall sustainability of the system. This cannot be achieved without formulating the interconnections associated with FEW components. This study formulates interconnections associated with FEW components. In an effort to increase the degree to which the results would generalize to FEW systems with different scales, the calculations of the study are performed for a sustainable FEW system that can consistently yield food for a family of four (two adults and two children) and supply its own water and energy needs from sustainable sources. Also, the sustainability is measured for two systems located in two different climates; one is relatively cloudy and humid and the other is sunny and arid. The results show that the highest sustainability improvement in both climates is associated with irrigation sub-component. Not only a sustainable water supply for irrigation sub-component improves the sustainability of water component, it also improves food sustainability and consequently energy sustainability. This finding can be explained by the fact that the irrigation sub-component is a resource supplier for grain sub-component, and that is a resource supplier for transportation fuel sub-component.

The energy consumption in a multi-chiller system is very significant and its consumption will be increased by inappropriate operating of the chillers. Accordingly, the economic dispatch in multi-chiller systems becomes an important issue for saving energy. Such issue is known as optimal chiller loading, which is solved by applying different optimization methods. This study employs exchange market algorithm to obtain optimal set points of the economic dispatch problem on chiller plants, which is inspired by trading the shares on a stock market. Exchange market algorithm is proposed for solving continuous non-linear optimization problems and it is comprised of two searching and two absorbing operators. The decision variable of the optimal chiller loading is partial load ratio of each chiller and the objective function of the optimal chiller loading problem is minimizing the power consumption while satisfying the cooling demand. This paper adopts three case studies to evaluate the performance of exchange market algorithm and compares it with other optimization methods applied on the optimal chiller loading problem. Results show that exchange market algorithm is an efficient method for solving optimal chiller loading problem in chiller plants, and is adaptable for usage in air-conditioning systems. In addition, the comparison results show that the exchange market algorithm provides better solutions than previous optimization methods applied on the optimal chiller loading method in terms of convergence speed and total consumption of electrical energy.

Phase change materials (PCMs) are being used as thermal mass in buildings to conserve energy. In this research, the application of appropriate PCMs to reduce building energy consumption and operating time of mechanical equipment during spring and autumn transition period in hot arid climate is studied. The study was conducted in buildings with diurnal occupation pattern. The simulation results were compared against measurements obtained from a calorimeter that was constructed for this purpose. The results show that PCM 29 °C and 5 air change natural ventilation rate reduce annual energy consumption by 15%. This means that while for 38 days in spring and 29 days in autumn, the mechanical equipment can be turned off. The thermal performance of PCM 25 °C is very well in spring. The appropriate PCM is the one with combined phase change melting point of 29 °C and 21 °C PCMs, which reduces energy consumption and increases the seasonal transition period 8% more than that of the single PCM 29 °C.

The short-term hydrothermal scheduling (SHTS) problem is defined as one of the most important and challenging scheduling problems, which aims to obtain the minimum cost of electrical energy production. Considering an insignificant generation cost of hydropower systems, the objective of STHS is to minimize the operation cost of thermal generation plants. The optimal generation scheduling of hydro and thermal units is a complex non-convex optimization problem taking into account a set of equality and inequality constraints. The challenging constraints of the STHS problem are valve-point loading effect of thermal units, power transmission loss of the system, generation capacity of thermal and hydro units, and load balance. In this paper, an improved harmony search (IHS) optimization algorithm is employed on the non-linear non-convex STHS problem. The proposed method is employed on two test systems in order to evaluate the performance of the applied optimization method on the STHS problem. The simulation results are provided and compared with those reported in recent publications, which proves the effectiveness of the proposed IHS method in the solution of the STHS problem.

Undoubtedly, coronavirus (COVID-19) has caused one of the biggest challenges of all times. The ongoing COVID-19 pandemic has caused more than 150 million infected cases and one million deaths globally as of May 5, 2021. Understanding the sentiment of people expressed in their social media comments can help in monitoring, controlling, and ultimately eradicating the disease. This is a sensitive matter as the threat of infectious disease significantly affects the way people think and behave in various ways. In this study, we proposed a novel method based on the fusion of four deep learning and one classical supervised machine learning model for sentiment analysis of coronavirus-related tweets from eight countries. Also, we analyzed coronavirus-related searches using Google Trends to better understand the change in the sentiment pattern at different times and places. Our findings reveal that the coronavirus attracted the attention of people from different countries at different times in varying intensities. Also, the sentiment in their tweets is correlated to the news and events that occurred in their countries including the number of newly infected cases, number of recoveries and deaths. Moreover, common sentiment patterns can be observed in various countries during the spread of the virus. We believe that different social media platforms have great impact on raising people’s awareness about the importance of this disease as well as promoting preventive measures among people in the community.

Nowadays, in several countries in the tropical islands of the Indian Ocean, including Madagascar island, Comores island, Seychelles island and Mayotte, no adopted and regulated building standards exist. Human health essentially depends on the quality of indoor air, and so several actions should be taken to solve this problem. The purpose of this study is to develop a database of thermal comfort in naturally ventilated buildings in order to improve indoor air quality, mainly in hospitals and shopping centers in the largest island of the Indian Ocean. To achieve this objective, and due to a lack of data regarding comfort in built environments in this region, experimental and subjective studies were carried out in 5 big hospitals and 50 small and large shopping centers, distributed in 25 districts of urban areas in Northern Madagascar. The adaptive approach was used for this purpose. A specific questionnaire based on the ISO7730 and 10551 was designed to collect these data. A total of 400 people participated in this study, and the survey was conducted during rainy and dry seasons. This study discusses the influence of gender, clothing, activities, voters’ mind state and occupants’ control strategies on adaptive comfort assessment. In addition, various comfort parameters were calculated for these buildings. Results show that, in both studied places, the lower and upper acceptable temperatures for 80% of the voters were 23.2 °C and 26.8 °C, while 90% of the customers and patients reported a comfortable temperature range of 24.5–26.2 °C. This will help to define proper guidelines to build more comfortable buildings in Madagascar and other countries of the Indian Ocean.

Purpose The purpose of this paper is to examine significant steps taken by the Pennsylvania State University (Penn State) to account for both energy cost savings and greenhouse gas (GHG) emissions reduction goals through strategic investments in energy conservation measures (ECMs) in campus buildings. Through an analysis of multiple years of investment in facility upgrades across the university, the impacts of ECMs of various types are characterized by building type. The standards and criteria for ECMs investments are also evaluated with the goal to develop a predictive tool to support decision making pertaining to an annual investment in a portfolio of ECMs that will maintain a trajectory to achieve both financial return on investment as well as GHG reduction goals. Design/methodology/approach This study is comprised of three main parts: analyzing the energy costs saving and GHG emissions reduction contribution of various building types in which ECMs were conducted, analyzing costs saving and GHG emissions reduction contribution of each ECM while considering the average annual investments made in them and estimating the impact of upgrading Penn State’s steam plants from firing a mixture of coal and natural gas to natural gas only on the GHG emissions. Findings These analyses help identify which types of buildings and ECMs would have larger savings and emissions reduction contributions. A calculator is also created to enable forecasting of costs saving and GHG emissions reduction of investment distribution strategy among ECMs. This study demonstrates that the calculator based on data from previous years will benefit decision makers in more wisely configuring the investment portfolio. Originality/value This paper fulfills an identical need to couple energy efficiency strategies coupled with the environmental impacts associated with different fossil fuel energy sources.

Thermal comfort and occupant thermal satisfaction are critical aspects in the indoor environment quality assessment and have received considerable attention by designers and building occupants. Improper indoor temperature not only decreases the level of occupant thermal satisfaction, but also has serious health related consequences. Despite the importance of occupant thermal satisfaction that has been vastly emphasized, studies incorporating occupants' satisfaction during the design process are very limited. Therefore, this study aims to develop a multi-objective design optimization model to minimize life cycle cost and life cycle emission, and maximize occupant satisfaction level in a typical commercial building. To solve the multi-objective optimization problem, a Harmony Search based algorithm is developed and employed. Moreover, to identify the level of design thermal satisfaction, a novel utility-theory based thermal comfort index is defined and calculated. A small office building is selected as a case study to analyze four different designs which are identified as optimum solutions. To determine the optimum designs, the satisfaction level of all the design combinations having cost and emissions similar to previously distinguished optimum solutions are compared and best designs are identified.

Energy systems worldwide are undergoing a major transformation as a consequence of the transition towards the widespread use of clean and sustainable energy sources. The electric power system in a sustainable future will augment the centralized and large-grid-dependent systems of today with distributed, smaller-scale energy generation systems that increasingly adopt renewable energy sources (e.g., solar and wind) and rely on cyber technologies to ensure resiliency and efficient resource sharing. Basically, this involves massive changes in technical and organizational levels together with tremendous technological upgrades in different sectors ranging from the energy generation and transmission systems down to the distribution systems. These actions constitute a huge science and engineering challenges and demands for expert knowledge in the field to create solutions for a sustainable energy system (both at the energy supply and demand sides) that is economically, environmentally, and socially viable while meeting high security requirements. On energy consumers’ side, useful and efficient energy services such as light, heating and cooling, cooking, communication, power, and motion are needed. These services are offered by specific equipment/devices, which use energy blocks either efficiently or inefficiently. Producing energy with high environmental, societal, or health risks is not a cheap way to meet such energy demand, but packages of efficient equipment and energy at least societal costs, which includes external costs, should be the ultimate objective to satisfy the needs of customers. At the supply side, there exists a bunch of opportunities for renewable energy technologies complemented with energy efficiency measures not only to provide local benefits, but also to contribute to sustainable development, which is framed in a three-pillar model: Economy, Ecology, and Society. Thus, the relationship between the use of renewables in energy mix and the sustainable developments goals (SDGs) can be viewed as a set of objectives and constraints that involve both global and local/regional considerations.

Currently, commercial buildings consume almost 18% of the total primary energy in the US and this is bound to increase with time due to population and economic growth. Small commercial buildings are those with less than 50,000 ft2 of conditioned area and represent 94% of total commercial buildings in the U.S. Since windows are responsible for 34% of commercial space conditioning energy use, windows in small commercial buildings should be given considerable attention. Besides the window material, selecting an optimum window dimension and location has played an important role in building energy performance. Since different weather conditions and sun angles impact on determining the optimum window dimensions and location, the goal of this study is to identify the optimum window design parameters in seven US climate zones in order to minimize building energy use. In this study, an optimization model using a genetic algorithm is developed and coupled with EnergyPlus software to identify the optimum window design parameters including a window to wall ratio, aspect ratio, and fenestration location. A double-story commercial building model, as a case study, is designed using DesignBuilder software to illustrate the application of the model. Identifying the optimum window dimensions and location in different locations would help engineers and designers to reduce building energy consumption in the early stages of the design. The results show that selecting optimum window dimensions and locations can reduce the total building energy consumption by 2 and 15% in cold and hot climate zones respectively.

Recently, interdependency between electricity, gas, and water infrastructures are increasing due to convincing reasons and critical dependence of societies to such infrastructures. In this paper, the interconnection of the natural gas, water and power generation systems is firstly described. Then, mathematical problem formulation of economic environmental dispatch as well as deterministic/probabilistic optimization of gas/water/power nexus models are presented with emphasis on their advantages/disadvantages and achievements. Moreover, different types of seawater desalination technologies and hydro/hydrothermal/pumped hydro storages are categorized and compared in short-term scheduling and planning studies. The expected outcomes and influence of the research will be attaining a comprehensive analysis of interactions among energy sources in multi-carrier energy systems as well as enhancement of performance of the integrated power, gas and water systems in terms of operation, economy and emission of pollutant gases including significant projects implemented around the world and case studies. The potential to increase efficiency, flexibility, and performance by considering multiple energy carriers are investigated in this study to highlight the remarkable opportunities that can be attained by investing in multi-energy generation assets. The current literature is categorized into three classifications including a) integrated electricity and gas sources, b) integrated electricity and water sources, and c) integrated electricity, gas and water infrastructures. Each of the mentioned multi-carrier energy systems is introduced by concentrating on the main contributions of the literature.

Cross-linked polyethylene (PEX) pipe has become viable alternative to traditional copper pipe to be used as residential water plumbing systems. PEX pipe is relatively cheaper and more versatile compared to a copper pipe. This study employs Life Cycle Assessment (LCA) to compare the economic and environmental performances of PEX and copper pipes over the life cycle of a student housing project in Lancaster, Pennsylvania. To achieve this objective, the costs in different life stages were extracted from three different sources in the market (RS Means book and two material suppliers; “Residential Copper Plumbing Products Limited Warranty” and “Uponor PEX”) and a Life Cycle Inventory (LCI) was used to quantify the energy and emissions of the two plumbing systems during the manufacturing process, in-service and end of life. The Building for Environmental and Economic Sustainability (BEES) model and SimaPro 8.0 software (Ecoinvent 3.0 database) were employed to develop the life cycle inventory of the piping system through all life cycle stages. BEES model adopts Tool for Reduction and Assessment of Chemical and other environmental Impacts (TRACI) life cycle impact assessment methods developed by the U.S. EPA Office of Research and Development. The LCA framework used in this study was based on a life cycle methodology that follows the International Organization for Standardization (ISO) 14,040 standard for LCA. The results of the analysis indicate that use of PEX piping instead of the traditional copper piping systems in residential buildings, would considerably reduce the total cost of the building up to 63% over its life cycle. Moreover, it can significantly reduce the carbon dioxide (CO2) emissions (approximately 42%) and lessen the negative effect of environmental impacts to land, water and air.

Traditional buildings throughout the globe provide instructive examples of sustainable solutions to building design problems. In recent years, researchers and architects have conducted different studies that represent the principles of Iran’s climate-responsive architecture. Yazd traditional buildings are examples of Iranian architecture that bear characteristics of environmentally friendly buildings. Thermal performance and comfort condition of these buildings in summer have been the matter of interest in recent studies. In this article, summer sections of Yazd traditional homes and their thermal behavior were studied. Yazd is located in hot and arid regions of Iran. EnergyPlus software program was used to investigate the thermal behavior of this house. To validate the developed model, a field study was carried out with lascar electronics data logger. The results reveal that internal temperature of summer sections in all seasons has less fluctuation than the outdoor temperature. Also, weather temperature in underground spaces is closer to the comfort temperature in comparison with spaces near to the ground floor. Therefore, to provide thermal comfort condition in these homes the least amount of non-renewable energy is needed.

The magnitude of building- and transportation-related GHG (greenhouse gas) emissions makes the adoption of all-EVs (electric vehicles) powered with renewable power as one of the most effective strategies to reduce emission of GHGs. This paper formulates the problem of GHG mitigation strategy under uncertain conditions and optimizes the strategies in which EVs are powered by solar energy. Under a pre-specified budget, the objective is to determine the type of EV and power generation capacity of the solar system in such a way as to maximize GHG emissions reductions. The model supports the three primary solar systems: off-grid, grid-tied, and hybrid. First, a stochastic optimization model using probability distributions of stochastic variables and EV and solar system specifications is developed. The model is then validated by comparing the estimated values of the optimal strategies and actual values. It is found that the mitigation strategies in which EVs are powered by a hybrid solar system lead to the best cost-expected reduction of CO2 emissions ratio. The results show an accuracy of about 4% for mitigation strategies in which EVs are powered by a grid-tied or hybrid solar system and 11% when applied to estimate the CO2 emissions reductions of an off-grid system.

Building and transportation sectors account for approximately 75% of CO2 emissions. A mitigation strategy that combines both renewable energy sources and adoption of all-electric vehicles (EVs) instead of conventional gasoline powered vehicles thus seems most promising. The desired outcome of this strategy cannot be achieved without taking into consideration human activities in building and transportation. This study compares the energy use in building and transportation, aiming to provide a proper approach for evaluating the effectiveness and possible CO2 emissions reductions of GHG mitigation strategies. The analysis of the collected data showed that on average each individual produced around 20 lbs of CO2 per day, 62% of the total CO2 was emitted in transportation. The mitigation strategy including EVs powered by electricity generated from coal-fired power plants resulted in average CO2 emissions reductions of 3.7%. The mitigation strategy using the EVs powered by solar energy obtained from grid-tied solar panels have led to 12.2% CO2 emissions reductions per day (from 12.38 lbs/day to 10.87 lbs/day), and the strategy incorporating EVs with the off-grid source of power was the most successful strategy and resulted in an average CO2 emissions savings of 12.38 lbs/day. This study also estimates the initial cost per pound of CO2 emissions reduction per day for each of the mitigation strategies.

The investigation of interdependency among energy carriers in different energy networks is beneficial for network operators in minimizing the operation cost of the whole networks and supplying the energy demands continuously. This study aims at evaluating the effectiveness of considering interconnections among energy sources, which is known as multi-carrier energy systems, on the operation of the energy networks. The proposed scheme aims to analyze the gas, power, heating, and water energy sources and their interdependencies in operation of multi-carrier energy networks considering the gas and power networks constraints and interdependent constraints of all the energy carriers. Accordingly, the role of combined heat and power technology, gas-fired power plants, pumped-storage systems, gas storage, heat buffer tank, and wind turbines as well as the power and gas network constraints are studied in the current work. The operation of multi-carrier energy systems in the presence of the above-mentioned technologies are studied to meet daily power, gas, heating and water loads. The proposed scheme is implemented in a case study to evaluate the practicality and performance of the scheme, which is analyzed by providing two cases for the selected multi-carrier energy network.

Transactive energy as an emerging approach and sustainable technology can provide an exceptional opportunity for microgrids to exchange energy with each other for greater benefits in the cluster mode. In this mode of operation, some collective and individual interests can be realized for the microgrids based on transactive energy management. This paper proposes mathematical models for microgrid clusters using a transactive energy structure to manage energy exchange in the smart grid. In order to make an informed decision for the operation of microgrid clusters, chance-constrained programming is employed to consider the uncertainties in balancing collective and individual interests under the transactive energy management. In this research, sixteen commercial microgrids are considered in the process of evaluating the efficiency of the proposed models using the chance-constrained programming method. Simulation results prove the effectiveness of the transactive energy approach accompanying the implementation of chance-constrained programming in energy management of the microgrid clusters.

Multi-chiller systems with thermal storage units are widely used to supply the cooling load of residential and commercial buildings. Due to the significant energy consumption, economic dispatch has been a crucial issue in multi-chiller systems. This paper presents a robust optimization approach for the day-ahead scheduling of a multi-chiller system with a chilled-water storage unit. The objective function aims to minimize the overall electricity cost of the system under hourly electricity prices, considering the uncertainty associated with the cooling load. Two case studies with different cooling load profiles are considered in evaluating the effect of the storage unit in increasing the robustness of total cost against the changes in the uncertain parameter. The multi-chiller system with the storage unit is modeled as non-linear programming solved with the General Algebraic Modeling System (GAMS), and the optimal partial load ratio of chillers and charge/discharge status of the storage unit are determined. In both case studies, the performance of the system with/without the storage unit is analyzed. The results demonstrate that the integration of the chilled-water storage unit increases the robustness of total cost against the variation of cooling load, as well as reducing the overall electricity cost.

The coordination of energy carriers in energy systems has significant benefits in enhancing the flexibility, efficiency, and sustainability characteristics of energy networks. These benefits are of great importance for multi-carrier energy networks due to the complexity of obtaining optimal dispatch, considering the non-convex nature of their energy conversion. The current study proposes a robust operation model for the coordination of multi-carrier systems, including electricity, gas, heat, and water carriers concerning thermal energy storage technology. Thermal energy storage is for storing extra heat generated by combined heat and power (CHP) plants and boilers in time intervals with low heat demand on the system and discharging it when required. Energy network operators should have the capability to manage uncertain energy loads to study the impact of load variation on the decision-making process in network operation. Accordingly, this study employs an information gap decision theory (IGDT) method to model the uncertainty of the power demand in optimal system operation. By applying the IGDT approach, the operator of the energy system can use the appropriate methodology to obtain a robust optimal operation. Such a modeling approach helps the operator to make suitable decisions about probable variations in power load. The introduced model is applied in a test system for evaluating the performance and effectiveness of the introduced scheme.

Alaska is at the forefront of climate change and subject to salient challenges including energy consumption. It is important to understand Alaskans' perceptions and opinions about energy consumption to solve Alaska's domestic energy problems and creating a sustainable future. However, it is challenging to collect public opinions about energy consumption using conventional survey methods, which are often expensive, labor-intensive, and slow. This study utilizes information-rich Twitter data to investigate Alaskans' perceptions and opinions on various energy sources and in particular clean energy sources. Using the geotagged Twitter data collected in Alaska from 2014 to 2016, a lexicon-based sentiment analysis approach was first applied to analyze the polarity in the expressed opinions. Further, a novel fuzzy-based theory is employed to derive the sentiment of the opinion in each tweet. The results indicate that there is a valuable growth rate for a set of energy-related keywords, such as “sun”, “power”, and “nuclear”. The rank of top 20 renewable energy-related keywords shows the word “Tidal” has the highest ranking followed by “solar panel”. Moreover, the attention to various types of energy is increasing dramatically among Alaskans. Importantly, Alaskans' attitudes toward energy and renewable energy changed positively from 2014 to 2016, indicating that Alaskans' energy choices are more acceptive towards or even favor renewable energy in the future.

This paper presents a two-stage stochastic programming model for optimal scheduling of the wind-thermal-hydropower-pumped storage system considering the competitive interactions between the electrical generation units. The first stage is focused on the day-ahead scheduling of the thermal power plants while the balancing market dispatch is considered in the second stage using the stochastic producers and quick dispatch units. To capture the uncertainties associated with electricity demand and wind speed, Latin hyperbolic sampling and fast forward selection methods are applied for scenario generation and reduction processes, respectively. The flexibility of the studied system is analyzed considering the variations of the key parameters of thermal units and the transmission line’s capacity. For this work, the modified IEEE 14-bus standard test system integrated with the components of the studied system is selected as the case study. After solving the problem, the maximum potential of clean energy production units is used in comparison with fossil fuel-based units through the optimal scheduling of the wind-thermal-hydropower-pumped storage system. Given the numerical results, reducing the flexibility of the system by reducing the ramp up/down parameters, increasing the minimum up/down parameters, and reducing the transmission line capacity has been led to increase of 6.47%, 7.3%, and 9.77% in the total energy cost, respectively.

Zero-energy building (ZEB) is a promising solution for dealing with the current issues of high pollutant gas emissions and substantial energy load, which is made possible by applying renewable energy resources and energy storage technologies. This paper proposes an innovative design of combined cooling, potable water and power ZEB using solar irradiance, which utilizes a solar dish Stirling engine for cogeneration of power and heat. The proposed structure uses the power output of the Stirling engine to drive the air blower and provide the energy load of the building. Also, to drive the water desalination process, the heat outlet of the Stirling cycle is utilized as a clean and sustainable energy resource. In a general description, the proposed design links a closed-air open-water humidification dehumidification (HDH) desalination process to solar Stirling engine and evaporator heat exchanger that makes the capability of attaining heat wastage during the power supply operation of the power unit and the building air conditioning apparatus. A nonlinear problem is simulated using a generalized algebraic mathematical modeling system and engineering equation solver to demonstrate the feasibility and robust performance of the proposed system in satisfying the cool, pure water and the electricity demands of a hotel building located in East Azerbaijan Iran.

Uncertainty of different parameters in power systems can put market players at risk. For example, uncertainty of market prices in energy markets with capitalist characteristics can negatively affect decisions of aggregators participating in such markets to make profits. In fact, uncertainty in capitalist energy markets should be controlled since many individuals who participate in such markets have specific expectations (economic goals in most cases) and this may result in some concerns over the future performances of mentioned markets under uncertainty. It should be noted that aggregator can be a person or an authority who is responsible for a group of energy consumers/resources like electric vehicles (EVs) and seeks to participate in energy markets in order to get to some expected goals that can be economical, technical and etc. Moreover, attitude to risk of a decision-maker can change in different working conditions which needs to be considered through various decision-making criteria. In this paper, robust performance (optimal/stable operation against the worst possible condition of uncertainty) of an EV aggregator against the uncertainty of market price is studied by incorporating robust optimization method. The aggregator willing to participate in energy market can benefit from the provided strategies created by this optimization model to satisfy the expected economic goals and control the uncertainty of energy market. In detail, specific plans and strategies are determined toward possible values of uncertain market price that satisfy economic goals of aggregator and keep the uncertainty under control. Mixed integer non-linear programming (MINLP) is used to model the problem which is solved in general algebraic modeling system (GAMS) software. A microgrid system composed of distributed generation units (local generation units that can be either renewable or non-renewable) including dispatchable and non-dispatchable ones, EVs and storage system is studied, and the results are presented. The results show that robust optimization model can minimize the daily market cost while mitigating possible risks for the aggregator of EVs. It should be noted that daily market accounts for energy market in which various energy resources are available to be selected by the related aggregator in order to supply energy demand.

A Solar Chimney (SC) is a passive system harnessing solar energy to ventilate inner spaces and improve occupants’ thermal comfort. The purpose of this study is to determine the time periods of a year most suitable for the use of a SC in terms of thermal comfort under the different climates of Bandar-Abbas (hot and humid), Yazd (hot and arid), Paris (mild and humid) and Toronto (cold and humid). To this aim, a thermosyphonic model – validated against published experimental data – was employed for running dynamic thermal simulations of a residential building using the EnergyPlus software. The optimal time periods of operation of the SC, driven by the values of the Predicted Mean Vote (PMV) between −0.7 and +0.7 and Predicted Percentage of Dissatisfied (PPD) below 15%, are found to be November and January to April in Bandar-Abbas (2265 h), while for the city of Yazd it comprises the months of April and May together with September and October (2145 h). As for Paris and Toronto, the period spans from June to August, for a total of 1065 and 1620 h respectively. Correlation coefficients disclosing the impact of the increased air mass flow rate revealed a positive impact everywhere during nighttime in terms of indoor air temperatures (from 0.02 in Bandar-Abbas to 0.77 in Yazd). On the other hand, the impact on indoor relative humidity is always negatively correlated, with a peak of −0.73 in Yazd. Year-long comfort conditions are finally displayed in psychometric charts against ASHRAE 55 Standard boundaries.

© 2018 American Society of Civil Engineers. Multiple energy-simulation programs have recently been developed. Most of these programs utilize thermodynamic equations and the mechanical characteristics, loadings, and temperature set points of the building to predict the total energy demand in a year. In addition, numerous building design parameters, including building envelope, window-to-wall ratio, and building orientation and shape, influence the level of energy consumption, which makes the design process complicated. Challenges that manifest within these systems are inherently complex and interdisciplinary in nature, and they often defy linear, cause-and-effect correlation, which makes the simulation of building energy performance even more complicated. In addition to the difficulty of determining the best design parameters, multiple numbers of objectives, such as the life-cycle cost and environmental emission of the project, increase the complexity of the problem. Therefore, a proper multiobjective optimization algorithm tool that is capable of eliminating a portion of trial-and-error process is needed. This article presents a framework for developing a multiobjective design optimization tool that is capable of identifying the designs with the lowest life-cycle cost, lowest life-cycle emission, and highest occupant thermal satisfaction. To demonstrate the application of this framework, the development of the design optimization tool using a C# program is presented. The building envelope, as the major barrier between the outdoor environment and inside conditioned zone, was considered as the main building component to optimize. To calculate the occupants' thermal satisfaction, a predicted mean vote method (PMV) was used. Even though the theoretical basis behind this tool is robust and accurate, the developed tool is simple, flexible, and user-friendly to encourage its use among designers and engineers. It is expected that the developed tool will ease the integration of energy efficiency in commercial buildings. The authors acknowledge the financial support for this study through a grant from the Qatar National Research Foundation (QNRF)/National Priorities Research Program (NPRP).

In their assumption of a static nature for occupants’ energy-use characteristics, the conventional energy-simulation tools have failed to bring about a reliable prediction regarding changes in occupants’ behavior and the resulting impact on energy consumption. Thus, this study investigated an agent-based approach to simulating the diffusion of energy-saving policies among the occupants and the related impact on energy consumption and emission production of commercial buildings. Two different strategies were implemented in the agent-based environment to track the changes in occupants’ behavioral patterns and the consequential energy conservation by providing informational feedback to the occupants. Compared to the results obtained from energy-simulation software, the first model showed a 13% variation/reduction in building energy consumption from Year 1 to Year 3; the second model, inspired by Bass diffusion theory, showed 20% variation/reduction in the same period. The results indicate that the ad...

Short-term hydro-thermal scheduling aims to obtain optimal generation scheduling of hydro and thermal units for a one-day or a one-week scheduling time horizon. The main goal of the problem is to minimize total operational cost considering a series of equality and inequality constraints. The problem is considered as a non-linear and complex problem involving the valve-point loading effect of conventional thermal units, the water transport delay between connected reservoirs, and transmission loss with a set of equality and inequality constraints such as power balance, water dynamic balance, water discharge, initial and end reservoir storage volume, reservoir volume limits and the operation limits of hydro and thermal plants. A solution methodology to the short-term hydro-thermal scheduling problem with continuous and non-smooth/non-convex cost function is introduced in this research applying dynamic non-linear programming. In this study, the proposed approach is applied to two test systems with different characteristics. The simulation results obtained in this paper are compared with those reported in recent research studies, which show the effectiveness of the presented technique in terms of total operational cost. In addition, the obtained results ensure the capability of the proposed optimization procedure for solving short-term hydro-thermal scheduling problem with transmission losses and valve-point effects.