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The power industry is the industry with the highest carbon emissions in China, and the rational allocation of carbon quotas for the power industry is the key to the rational allocation of carbon quotas in China, while the transfer of carbon emissions generated by inter-provincial power trading makes it difficult to calculate the carbon emission quotas for power. The article calculates the comprehensive power carbon quota for each province based on the principles of equity(PEQ) and the principles of efficiency(PEF). The results of the study prove the following points. (1) There are significant differences in the carbon emission factors calculated for electricity generation and consumption in each province. (2) The western provinces with abundant electricity resources deliver electricity to the eastern and southern provinces to ensure their economic development, so the western provinces with higher inflows of electricity carbon emissions bear the carbon emission pressure of the eastern and southern provinces. (3) The provincial electricity carbon quota calculated by the principle of comprehensiveness(PC) takes into account social equity and economic efficiency, and the calculation results are more reasonable. This study achieves balanced regional development by reasonably allocating provincial electricity carbon quotas. • There are significant differences between the calculated carbon emission factors for electricity generation and consumption in the provinces. • The western provinces with larger inflows of carbon emissions from electricity bear the pressure of carbon emissions from the eastern and southern provinces. • The provincial power carbon quota allocation scheme under the principle of comprehensive combines the advantages of the responsibility principle and the efficiency principle. [ABSTRACT FROM AUTHOR]

Vehicle-to-grid technology (V2G) is a novel large scale energy storage option to improve the grid integration of renewable energy sources (RES). Using electric vehicle (EV) batteries to store and provide electricity to the grid, the intermittency of RES can be reduced. However, a successful implementation of this technology depends on various social and technological factors. This study analyses the influence of social and technical factors such as V2G acceptance (percentage of EV users that allow V2G energy transfer), EV battery availability for V2G service (percentage of the EV battery), EV adoption level (number of EVs in EV fleet) and the charger power for the energy transfer between the storage system and grid. Using Germany for the case study, a simulation model is developed and employed for the study. The base simulation results show that Germany needs 190 GW of PV and 170 GW of wind turbine installation to meet 80 % of electricity generation from RES in 2030. Further results show that an increased V2G acceptance and EV battery availability reduces the V2G contribution from individual EVs. A V2G acceptance of only 30 %, using half of the battery capacity dedicated to V2G, can help to reach an hourly reliability of 86.6 %. Having 50 % V2G acceptance, the hourly reliability increases by 5.3 %–91.9 %. The final analysis on charger power and EV adoption level highlights that a normal charger power of 7 kW or 11 kW can successfully accommodate V2G service. The study's overall results indicate that V2G technology will be an effective storage solution for Germany in the future. [Display omitted] • 50 % V2G acceptance and EV battery availability provides 92 % hourly grid reliability. • Germany could meet 71.28 % of its electricity needs in 2030 from 190 GW of PV and 170 GW of wind with adequate V2G support. • Moderate-power AC chargers (7 kW or 11 kW) are adequate for V2G services. • Larger EV fleets not only provide more storage capacity, but also increase the electricity demand. • Overall result indicate V2G as an effective storage solution for the future. [ABSTRACT FROM AUTHOR]

Seasonal climate variations affect electricity demand, which in turn affects month-to-month electricity planning and operations. Electricity system planning at the monthly timescale can be improved by adapting climate forecasts to estimate electricity demand and utilizing energy models to estimate monthly electricity generation and associated operational costs. The objective of this paper is to develop and test a computationally efficient model that can support seasonal planning while preserving key aspects of system operation over hourly and daily timeframes. To do so, an energy system optimization model is repurposed for seasonal planning using features drawn from a unit commitment model. Different scenarios utilizing a well-known test system are used to evaluate the errors associated with both the repurposed energy system model and an imperfect load forecast. The results show that the energy system optimization model using an imperfect load forecast produces differences in monthly cost and generation levels that are less than 2% compared with a unit commitment model using a perfect load forecast. The enhanced energy system optimization model can be solved approximately 100 times faster than the unit commitment model, making it a suitable tool for future work aimed at evaluating seasonal electricity generation and demand under uncertainty. • An energy system optimization model is repurposed for seasonal planning. • Results are compared with a unit commitment model under two demand forecasts. • The enhanced energy system model can generate results nearly 100 times faster. • This model performance makes it possible to do seasonal planning under uncertainty. [ABSTRACT FROM AUTHOR]

Long term time series of variable renewable energy (VRE) generation and electricity demand (load) provide important insights into the feasibility of fully renewable power systems. The coverage of energy statistics is usually too short or the temporal resolution too low to study effects related to interannual variability or the impact of climatic extreme events. We use time series simulated from climate data to assess the frequency, duration, and magnitude of extreme residual load events of two fully renewable power scenarios with a share of VRE generation (wind and solar PV) of about 50% for the case of Sweden. We define residual load as load – wind – PV – nuclear generation. Extreme residual load events are events that exceed the balancing or ramping capacities of the current power system. For our analysis, we use 29 years of simulated river runoff and wind and PV generation. Hourly load is derived from MERRA reanalysis temperature data by applying statistical models. Those time series are used along with historic capacity and ramping restrictions of hydro and thermal power plants in an optimization model to minimize extreme residual load events. Our analysis shows that even highly flexible power systems, as the Swedish one, are affected by climatic extreme events if they increase their VRE shares. Replacing current nuclear power capacities by wind power results on average in three extreme residual load events per year that exceed the current power system's flexibility. Additional PV generation capacities instead of wind increase the number of extreme residual load events by about 4%, as most events occur during the winter month when solar generation is close to zero and thus not able to counterbalance low wind events. Contrarily, overproduction and the need to curtail VRE generation become more pressing with higher shares of PV. In the discussion we highlight measures that could provide additional balancing capabilities to cope with the more frequent and severe residual load events in a fully renewable power system with high shares of VRE generation. • Load during winter is negatively correlated with wind power and natural river runoff. • Annual residual loads vary between 51 and 72 TWh in a fully renewable power system. • Two out of three years exceed the balancing capacities of the current power system. • The longest extreme residual load event lasts 18 h with a maximum lack of capacity of 4 GW. • Additional flexibility measures can avoid loss of load events in fully renewable power systems. [ABSTRACT FROM AUTHOR]

In today's China, the reverse distribution of electricity resources and electricity demand makes large-scale and long-distance transmission of electricity inevitable, resulting in the problem of electricity allocation optimization. To address this problem in the region covered by the China Southern Grid (CSG), the paper establishes an optimized model for electricity generation and interprovincial trading strategies in southern China. Based on data from 2015, this paper simultaneously optimizes electricity generation and interprovincial trading strategies in each of the five provinces. The result shows that interprovincial trading in the CSG-served area can yield significant energy, economic, environmental and interconnection benefits. It can reduce the power supply cost and environmental pressure in Guangdong province, help make Yunnan province a hydropower base in China and a thermal power base in southern China, facilitate the completion of the "West-to-East Power Transmission" project, and partly resolve the issues raised in each province's 13th Five-Year Plan as well as the problem of optimizing electricity allocation in the region covered by the CSG. • We propose an annual total optimized model for electricity generation and interprovincial trading in Southern China. • The model optimizes the electricity trading and transmission routes across regions. • The model considers the generation cost and penalty cost of emitting CO 2 , SO 2 and NO x. • Corresponding policies and proposals are put forward for power grid and five regions in South China. [ABSTRACT FROM AUTHOR]

Abstract Electricity users are the major players of the electricity systems, and electricity consumption is growing at an extraordinary rate. The research on electricity consumption behaviors are becoming increasingly important to design and deployment of the electricity systems. However, the acquisition of data related to the electricity consumption behaviors is still a major challenge. Data synthesis is among the best approaches to solving the issue, and the key is the model that preserves the real electricity consumption behaviors. In this paper, we propose a hierarchical multi-matrices Markov (HMM) model to synthesize scalable electricity load curves that preserve the real consumption behaviors on three time scales: per day, per week, and per year. To promote the research on the electricity consumption behaviors, we use the HMM approach to modeling two distinctive raw electricity load curves. One is collected from the resident sector, and the other is collected from the non-resident sectors, including different industries such as education, finance, and manufacturing. The experiments show our model performs much better than the cluster-based Markov model. We publish two trained models online, publicly available from http://www.benchcouncil.org/electricity , and researchers are allowed to directly use these trained models to synthesize scalable electricity load curves for further research. Highlights • An adaptive K-Means algorithm was developed to cluster electricity load curves. • A novel hierarchical multi-matrices Markov (HMM) model was proposed. • The HHM model preserved the properties of the electricity load curves. • Two trained HMM models was published to synthesize scalable electricity load curves. [ABSTRACT FROM AUTHOR]

Abstract Based on weather and electricity demand data for the period 1984–2013, we develop a system model based on energy balance to determine the size of photovoltaic and wind generation combined with energy storage to provide a firm power supply for Great Britain. A simple graphical methodology is proposed where the required wind and PV generation capacities can be read off from a "system configuration diagram" as a function of the available storage size. We show, by way of illustration, that a reliable supply would be produced by a system based on PV and wind generators generating some 30% more electrical energy (approximately 100 TWh p.a.) than the current electricity supply system if supplemented with 30 days of storage. In terms of generation capacities, the current 82 GW of principally thermal generation would then be replaced by about 150 GW of wind turbines and 35 GW of PV arrays. Highlights • Great Britain power system consisting of wind turbines, photovoltaics and storage. • 100% renewable system producing 30% more electrical energy than the demand. • 30 storage days (25 TWh) for uninterrupted power supply over a 30 year design period. [ABSTRACT FROM AUTHOR]

The generation forecast helps to develop the scheduling plan of power sources, build a power balance system and ensure the adequacy of power supply to the power system at the macro level. Therefore, a novel structurally adaptive discrete grey Bernoulli model, called DHGBM(1,1), is proposed in this paper for the prediction of power generation. Firstly, the Hausdorff fractional order cumulative generation operation makes it possible to achieve new information prioritisation. As for the structure of the model, we have extended the nonlinear grey Bernoulli model with the introduction of a nonlinear dynamic structure term, which allows the model to simulate variable generation data and improves the adaptability of the model. In a comparative test of the algorithms, the well-performing differential evolution algorithm was used for hyperparameter optimisation of the model to obtain better predictive performance. Monte Carlo simulations and probability density analysis verified the excellent robustness of the model. In addition, through the simulation tests, we obtained a reasonable range of parameters for the structural terms, thus limiting the complexity of the model, and the phenomenon of model overfitting was effectively prevented. Finally, five actual cases of China's hydro, China's thermal, China's nuclear, China's wind power generation and China's electricity exports are predicted. • A novel nonlinear dynamic structural term is proposed. • A novel structural adaptive discrete grey Bernoulli model is developed. • The robustness analysis of the novel model is established. • Comparative study shows that the novel model is superior to other six models. [ABSTRACT FROM AUTHOR]

The difference in electricity pricing based on the time of power use has led to load shifting from peak to off-peak hours in hybrid power systems (HPS). Apart from optimising electricity cost, shifting of the load may also change the capacity of storage in the system. Power Pinch Analysis has been recently applied to guide load shifting aiming to minimise the cost of electricity, considering the peak and off-peak electricity pricing. The HPS was assumed to be ideal with 100% efficiency, which is not achievable in actual operational condition. This work extends the study by scrutinising the effects of peak-off-peak load shifting on the capacity of storage in HPS. The effects of energy losses due to the inefficiency during power conversion, transfer and storage in the HPS are considered in developing shifting heuristics to ensure optimal storage size is achieved. Implementation of the proposed load shifting strategy on a case study demonstrates that a reduction of up to 30% in the storage size can be achieved, which led to a minimum storage cost. The distribution of peak hours' demand to off-peak hours also successfully provide significant savings in the electricity bill. [ABSTRACT FROM AUTHOR]

This research analyzes proposed determinants of electricity consumption in Jordan during the period 1986–2015. For the upcoming years, Jordan's electricity consumption projections expected to exceed electricity generation capacities. Therefore, to understand electricity consumption key determinants, six independent variables were used: Gross Domestic Product (GDP), electricity prices, population, urbanization, structure of economy, and aggregate water consumption. A multivariate model was constructed using annual data to examine the influence of the independent variables on electricity demand. Johansen Cointegration test is used to examine the long-term relationships in the model, and Vector Error Correction Model (VECM) is utilized then. VECM regression results show that GDP, urbanization, structure of economy and aggregate water consumption are significant and positively related to electricity consumption, while electricity prices are significant and negatively related to electricity consumption. Population showed significant positive effect on electricity consumption in the short run. The result of VECM implies that there is a need to invest more in green energy projects, ban importing low efficiency electrical appliances and review refugee reception policy in place. [ABSTRACT FROM AUTHOR]

A constraint to represent maximum likelihood of occurrence for coexistent variable demand and variable generation is proposed for inclusion in the conventional static load dispatch. The constraint allows direct representation of cross-correlations between variable power levels, which makes the dispatch appropriate for scenarios with high penetration of renewable power generation. At optima, the augmented dispatch converges to utility generation cost that is “most likely to occur”; and can therefore provide useful support for generation resource planning by accommodating demand and generation variability. Formulation of the maximum likelihood load dispatch, as well as Karush-Kuhn-Tucker conditions for its optima, are presented in the paper. Features of the optima are established by three-generator and twenty-generator case studies that represent both centralised and distributed variable generation scenarios. [ABSTRACT FROM AUTHOR]

Coal-fired thermal power plants (TPPs) had been generally designed to cover base loads of electricity consumption. Nowadays, they should be flexible to participate in the primary frequency control of electric system, especially because of increased number of wind and solar plants with intermittent electricity production. In this paper a new method for the primary power control in TPPs is presented, based on the feedwater flow rate changes between the feedwater heaters (FWHs) and an additional high pressure economizer. The achieved primary power control reserve is demonstrated by measurements at the lignite-fired 650 MWe TPP after its retrofit by the installation of the high pressure economizer in parallel with FWHs. In addition, the TPP transient operation during the primary control are simulated and analyzed with the developed thermal-hydraulic model of the steam turbine heat regeneration system. The control reserve of approximately 10 MWe is shown both by measured data and results of numerical simulations. The control reserve is originally provided by the fresh steam throttling in front of the turbine, but this throttling can be reduced by the application of the new method. The rates of change of wall temperatures of FWHs tubes are within allowed limits during the transients. [ABSTRACT FROM AUTHOR]

There is an increasing integration of distributed natural gas-fired electric power generations across the globe. The natural gas-fired power generators are dependent on the availability of reliable natural gas distribution systems. This interdependency coupled with the scalability of the natural gas-fired distributed power generators presents the management of an energy utility with real options to execute, delay or abandon long-term expansion plans based on new realities of electric power demand. A real options analysis of these flexibilities is carried out in this paper. The assessment of these options includes the identification and valuation of the identified options. The value of flexibilities and options is illustrated by assessing the options to implement, delay or abandon expansion plans in the presence of uncertain electric power demand on 9 and 33 bus electricity distribution systems. [ABSTRACT FROM AUTHOR]

This paper analyzes tradable green certificate markets, where producers investing in new renewable capacity receive certificates based on their production. These are sold to electricity retailers, who are required to buy certificates in an amount proportional to their total sales. To assess the performance of such a scheme, we develop a stochastic model based on dynamic programming. Considering the case of the Swedish–Norwegian electricity certificate market, the main findings include: (i) price expectations are not formed easily, and hence, investment decisions must be made under considerable uncertainty; (ii) under the current market structure, prices are expected to start at today's level, while decreasing steadily towards zero when approaching the planned end of the market; (iii) the prices are highly sensitive to changes in electricity consumption and generation; (iv) regulatory changes should be implemented carefully to avoid increased uncertainty and a consequent increase in price volatility; and (v) the current policy of having a price-based penalty does not soften the two-state nature of this market. So far, the market has shown ample ability to promote cost-efficient investment in renewable electricity production. [ABSTRACT FROM AUTHOR]

A bi-generation system combining direct absorption flat plate solar collector for medium temperatures air-heating applications integrated with a thermoelectric generator was modeled under optically concentrated solar radiation conditions. In order to improve the performance of the bi-generation system, the proposed system was simulated under the effect of evaporative cooling at the cold side of the thermoelectric modules in order to create a cooling effect that decreases the temperature of the cold junction, thus enhancing the electrical conversion efficiency of the thermoelectric modules. Rung-Kutta 4th order method is used to solve the ordinary differential, equation, while Newton-Raphson iterative technique is used to solve the nonlinear algebraic system of the model governing equations. The results have reflected a significant effect for the evaporative cooling on the system performance particularly at high values of optical concentration ratio. Furthermore, an augmentation of 19.13% in the total electrical power output was predicted at a concentration ratio of 20 suns. Simulation results had also shown that more stable electrical performance of the system when adopting evaporative cooling method compared to forced convection. It was found that there was an optimum value of water mass flow rate, at which the maximum power output was generated. Finally, relative humidity was shown to have a less significant impact on the performance at high temperature operation, since the effect of high water vapor partial pressure at the heat sink (cold junction), when the system is operating at elevated temperature, dominated over the effect of ambient relative humidity according to the evaporation rate governing model. [ABSTRACT FROM AUTHOR]

Co-locating electricity storage with demand has significant potential to increase consumption of locally-generated electricity, defer infrastructure investments, and contribute to the task of balancing supply and demand on the wider network. In the UK, unlike domestic consumers, large enterprises are already incentivised to reduce peak demand through exposure to time- and demand-dependent network charges. This paper considers the potential of electricity storage to reduce the bills of large enterprises, focusing on Lancaster University as a case study. Through analysis of Lancaster University's recent demand and generation data and current and future charges, it is shown that recent widening of red distribution charge time bands has reduced the value of electricity storage to enterprises, and that in 2015 an enterprise such as Lancaster University could have expected electricity storage to deliver annual savings of around £27 per kWh of storage capacity, by reducing network charges. An analysis of these charges around Great Britain shows that the opportunity for storage to provide savings to enterprises is greatest in the south-west (at least £70/kWh.yr in 2017) and lowest in the north of Scotland (at least £20/kWh.yr). Whether investment in storage provides positive value to enterprises is shown to be strongly dependent upon location. [ABSTRACT FROM AUTHOR]

As the tropics and subtropics become increasingly urban, industrial and affluent, energy demands for thermal comfort may evolve differently than they have historically across the global North. Already, heating, ventilation and air conditioning account for 35% of total primary energy in the United States, and are expected to reach similar proportions in China within 5 years. With increasing population in high temperature areas, electricity demand for increased air-conditioning usage may drive extreme electricity peak demands and total usage. This paper presents comparative estimates of peak and annual electric cooling and heating electricity usage at the city-scale, including both OECD and non-OECD member cities. Our results indicate that mature urban economies of the OECD exhibit a cooling electricity response of 35–90 Watts per °C per capita above room temperature for cooling (interquartile range of estimates). Tropical/subtropical cities outside the OECD (mostly in South Asia, Africa and the Middle East) currently demand just 2–9 W/°C/capita, indicating significant growth in temperature-dependent electricity demand as air conditioning is adopted. A similar story is unfolding on the heating side, with subtropical cities adopting electric resistive heaters, potentially precipitating additional electricity generation and delivery concerns, particularly electric resistance heating is adopted instead of heat pumps. [ABSTRACT FROM AUTHOR]

Long-range energy scenarios are commonly used to inform national energy policy decisions. Although the scenario approach aims to expand the spectrum of futures considered, in the past energy scenarios have not been diverse enough to include less expected real-world developments. We use a bottom-up energy system model EXPANSE with Modeling to Generate Alternatives (MGA) to assess the diversity of the existing ensemble of multi-organization, multi-model Swiss electricity supply scenarios. We show that both for 2035 and 2050 existing scenarios cover well the various possibilities in deployment of individual electricity generation technologies in terms of installed capacity or produced electricity. When analyzing scenarios as a whole, we find that in 2035 there is a lack of scenarios with high electricity demand and greater deployment of renewable technologies occurring simultaneously, as well as scenarios with a negligible or negative share of net electricity import. In 2050, there is also a lack of scenarios that depict high electricity demand and conservative deployment of new renewable technologies, especially solar PV. We propose six additional scenarios to be included to increase the diversity of the ensemble of Swiss electricity supply scenarios and achieve better understanding of the future possibilities and uncertainties. [ABSTRACT FROM AUTHOR]

Long term peak electricity demand forecasting is a crucial step in the process of planning for power transmission and new generation capacity. This paper discusses an application of the Generalized Pareto Distribution to the modelling of daily peak electricity demand using South African data for the period 2000 to 2010. The main contribution of this paper is in the use of a cubic smoothing spline with a constant shift factor as a time varying threshold. An intervals estimator method is then used to decluster the observations above the threshold. We explore the influence of temperature by including it as a covariate in the Generalized Pareto Distribution parameters. A comparative analysis is done using the block maxima approach. The GPD model showed a better fit to the data compared to the GEVD model. Key findings from this study are that the Weibull class of distributions best fits the data which is bounded from above for both stationary and non-stationary models. Another key finding is that for different values of the temperature covariate the shape parameter is invariant and the scale parameter changes for different values of heating degree days. [ABSTRACT FROM AUTHOR]

Demand response (DR) can serve as an effective tool to better balance the electricity demand and supply in the smart grid. It is defined as "the changes in electricity usage by end-use customers from their normal consumption patterns" in response to pricing and incentive payments. This paper focuses on new opportunities for DR with electric vehicles (EVs). EVs are potential distributed energy resources that support both the grid-to-vehicle and vehicle-to-grid modes. Their participation in the time-based (e.g., time-of-use) and incentive-based (e.g., regulation services) DR programs helps improve the stability and reduce the potential risks to the grid. Smart scheduling of EV charging and discharging activities also supports high penetration of renewables with volatile energy generation. This paper proposes a novel stochastic model from the Independent System Operator's perspective for risk management and participation planning of EVs in the smart grid for DR. The risk factors considered in this paper involve those caused by uncertainties in renewables (wind and solar), load patterns, parking patterns, and transmission lines' reliability. The effectiveness of the model in response to various settings such as the area type (residential, commercial, and industrial), the EV penetration level, and the risk level has been investigated. [ABSTRACT FROM AUTHOR]

The great majority of Hungarian electricity generating system's capacity is obsolete and needs to be replaced. Current production is dominated by nuclear power and coal, whilst depending heavily on imported electricity to cover demand. Official plans for the future envisage scenarios also greatly dependent on fossil fuels and/or imported electricity. Currently, there are no specific plans for the large-scale introduction of renewable energy sources or energy independence. This paper shows that it is possible – with no significant change in structure – to develop an electricity power system for the country using a significant amount (25–30%) of renewable sources, which is less dependent on non-domestic sources for generation and which is more environment friendly than the official, forecast scenarios. [ABSTRACT FROM AUTHOR]

Traditional electricity supply planning models regard the electricity demand as a deterministic parameter and require the total power output to satisfy the aggregate electricity demand. But in today's world, the electric system planners are facing tremendously complex environments full of uncertainties, where electricity demand is a key source of uncertainty. In addition, electricity demand patterns are considerably different for different regions. This paper developed a multi-region optimization model based on two-stage stochastic programming framework to incorporate the demand uncertainty. Furthermore, the decision tree method and Monte Carlo simulation approach are integrated into the model to simplify electricity demands in the form of nodes and determine the values and probabilities. The proposed model was successfully applied to a real case study (i.e. Taiwan's electricity sector) to show its applicability. Detail simulation results were presented and compared with those generated by a deterministic model. Finally, the long-term electricity development roadmap at a regional level could be provided on the basis of our simulation results. [ABSTRACT FROM AUTHOR]

The compressed air sector (CAS) is currently receiving great attention, being responsible for an electric energy consumption equal up to 10% world-wide overall industry electricity needs and, accounting for all the other fluid compressor uses, up to 20% overall electricity needs. Consequently, the saving potential in CAS, usually 30–35% overall consumption, can be compared with the one of other energy measures (e.g. efficiency in other sectors, renewable energy generation, etc…) and the great potential toward the 20-20-20 European target can be appreciated. Since the compressor technology alone accounts for a mean 30% CAS overall electricity consumption, it appears vital to pay attention to both the machines specific consumption and the spread in performances, among manufacturers. Based on data from the Compressed Air and Gas Institute and Pneurop, the potential saving simply due to compressor replacement can be estimated, as well as the directions of a further energy saving in current technology. One of the most important limiting factors, which not only prevents the substitution of existing energy inefficient machines, but also affects the choice in a preliminary phase, is the payback time of the additional investment, required by a more efficient compressor purchase. Since the eligibility of a measure for actual application hinges on its benefits-to-costs ratio, rather than on its energy merit alone, the present study investigates, with reference to the current industrial CAS, the relationship between the energy cost reduction from an efficiency increase through compressor replacement, and the required additional investment, based on typical operating pressures, flow rates and machine types of interest for current applications. [ABSTRACT FROM AUTHOR]

To achieve optimal generation from a number of mixed power plants by minimizing the operational cost while meeting the electricity demand is a challenging optimization problem. When the system involves uncertain renewable energy, the problem has become harder with its operated generators may suffer a technical problem of ramp-rate violations during the periodic implementation in subsequent days. In this paper, a scenario-based dynamic economic dispatch model is proposed for periodically implementing its resources on successive days with uncertain wind speed and load demand. A set of scenarios is generated based on realistic data to characterize the random nature of load demand and wind forecast errors. In order to solve the uncertain dispatch problems, a self-adaptive differential evolution and real-coded genetic algorithm with a new heuristic are proposed. The heuristic is used to enhance the convergence rate by ensuring feasible load allocations for a given hour under the uncertain behavior of wind speed and load demand. The proposed frameworks are successfully applied to two deterministic and uncertain DED benchmarks, and their simulation results are compared with each other and state-of-the-art algorithms which reveal that the proposed method has merit in terms of solution quality and reliability. [ABSTRACT FROM AUTHOR]

Transition to a low-emission society requires radical emissions reductions in the electricity generation sector. Imposing that hourly load has to be served solely by wind power, solar PV and an energy storage technology, it is shown, using optimization and simulations of re-analysis data, how the EU can design a completely CO 2 emission-free electricity generation sector. For such a system, the optimal battery energy capacity should be 32 times of average hourly consumption of electricity in the EU. However, total production of electricity over a 10 year period is as much as 43% above total consumption of electricity. • Modeling of a CO2 free electricity system in Europe. • Using optimization and simulations of spatial re-analysis data. • Battery capacity should be 32 times of average hourly consumption of electricity. • Total production of electricity has to exceed total consumption by 43%. [ABSTRACT FROM AUTHOR]

Development of MCESs (Multi-Carrier Energy Systems) can make the energy supply more reliable and efficient. Due to the redundancy potential of these systems, in case of an energy carrier interruption, consumers may be able to supply part of their loads from other energy infrastructures. This paper presents an approach for evaluating the adequacy of MCESs considering the dependencies of energy carriers at both generation side and demand side. To model the adequacy of generation systems supplying different forms of energy, a new methodology is presented, which considers the limitation of primary resources and takes into account the COPTs (Capacity Outage Probability Tables) of different energy infrastructure components. The model incorporates the impact of load dependencies at the demand side. A new focus is set up, by considering the outputs as services that can be provided through different types of energy supply. Illustrative results are presented to show the application of the proposed models to multi-energy systems. [ABSTRACT FROM AUTHOR]

Proper planning for the dimensions of an electricity production and transmission system requires the availability of medium- and long-term electricity demand projections that are sufficiently reliable. Generally, these projections are directly linked to the estimated growth for the whole real GDP (gross domestic product), although an in-depth historical evolution of this demand, as that given in this article, advises the explicit consideration of several determinants. The aim of this paper is to present an alternative analysis of the demand for electricity based on a simple growth rate decomposition scheme that allows the key factors behind this evolution to be identified. It is possible, taking this scheme as a starting point, to develop a long-term forecasting model to obtain projections of electricity demand given the expected evolution of the key factors. The proposed methodology is illustrated using Spain as a case study to obtain demand projections till 2030. [ABSTRACT FROM AUTHOR]

This paper investigates the impacts of California, USA reaching its renewable electricity target of 33%, excluding large hydro, by 2020, which is set out in the state's RPS (Renewable Portfolio Standard). The emerging renewable electricity mix in California and surrounding states which form the WECC (Western Electricity Coordination Council) is analysed using the CEPA (Carbon Emission Pinch Analysis) and EROI (Energy Return on Energy Invested) methodologies. The reduction in emissions with increased renewables is illustrated and the challenge of maintaining high EROI levels for renewable generation is examined for low and high electricity demand growth. Results demonstrate that wind and solar PV collectively form an integral part of California reaching the 33% renewables target by 2020. Government interventions of tax rebates and subsidies, net electricity metering and a four tiered electricity price have accelerated the uptake of electricity generation from wind and solar PV. Residential uptake of solar PV is also reducing overall California electricity grid demand. Emphasis on new renewable generation is stimulating development of affordable wind and solar technology in California which has the added benefit of enhancing social sustainability through improved employment opportunities at a variety of technical levels. [ABSTRACT FROM AUTHOR]

This paper presents an extension of a MPC (Model Predictive Control) approach for microgrid energy management which takes into account electricity costs, power consumption, generation profiles, power and energy constraints as well as uncertainty due to variations in the environment. The approach is based on a coherent framework of control tools, like mixed-integer programming and soft constrained MPC, for describing the microgrid components dynamics and the overall system control architecture. Fault tolerant strategies are inserted in order to ensure the proper amount of energy in the storage devices such that (together with the utility grid) the essential consumer demand is always covered. Simulation results on a particular microgrid architecture validate the proposed approach. [ABSTRACT FROM AUTHOR]

Price guidance in the flexible energy electricity market is not mature. The imbalance between supply and demand in current electricity markets is mainly caused by uncontrolled flexible energy generation; the electricity supply with load changes in the traditional electricity market is no longer applicable to the current flexible energy electricity market. The work proposes neural network dynamic differential control as the long-term price guidance mechanism for flexible energy service providers. This mechanism alleviates the imbalance between supply and demand in the future electricity market. The neural network dynamic differential control method combines the long-short term memory network and the stochastic dynamic differential method. The proposed method integrates the effects of environmental temperature, holidays, and government regulation on demand. This mechanism enables the demand of flexible energy customers to equal the generation capacity of flexible energy, customers to minimize costs in a long-term electricity market and flexible energy to be completely absorbed. This mechanism directly controls the electrical energy demand and indirectly controls the indoor temperature of the building cooling model through price guidance signals. The experimental results show that the building cooling model can save 4.84% of the cost and 65.03% of the potential cost savings. • A neural network dynamic differential control method is proposed. • The method considers the influence of multiple factors on the electricity demand. • An operational framework for flexible energy service providers is constructed. • A generally applicable long-term price guidance mechanism is proposed. • The mechanism saves the electricity cost of customers. [ABSTRACT FROM AUTHOR]

Capacity expansion models for the power sector are used to project future decisions over the coming decades by simulating investment and operation decisions for the use of electricity. Due to model performance constraints, these models typically do not explicitly simulate every hour within a year, but instead simulate representative time segments (groups of hours). This paper evaluates different approaches for selecting time segments across three methods: sequential, categorical, and clustering, across a wide range of time-segment quantities, for a total of 204 temporal profiles. To measure the performance of each profile's ability to accurately represent data, the root-mean-square-error of each profile's time segments are compared to the data's original hourly data. The temporal alignment across regions is also measured (i.e., how often windy days align across regions). Different spatial resolutions were applied for a subset of the temporal selection methods to investigate the impact spatial resolution has on performance. This paper provides a framework for measuring the value of different temporal selection methods and of adding more granular data to energy system models. Overall, multi-criteria clustering yields the lowest root-mean-square-error across all datasets evaluated and provides a holistic view of the intertwined relationships between renewable generation and electricity demand. • Temporal selection methods analyzed across three commonly used techniques. • Measured performance of representing model data within representative hours. • Measured temporal performance across electric load and wind and solar generation. • Clustering techniques performed better than sequential or categorical techniques. [ABSTRACT FROM AUTHOR]

Operational efficiency analysis is the foundational work for the management policy adjustment of the electric power industry in China and many other countries, but it is difficult to conduct because of the system complexity. This research uses a network structure to simulate the operating process of this industry. Through introducing the dynamic slack-based measure algorithm into this structure, the operational efficiencies of the entire industry and each stage (power generation and transmission–distribution) in each period are evaluated. On this basis, extended methods are also designed to measure the factor efficiency and the technological gap of each group. The model is applied to China's electric power industry, and the following conclusions are drawn: (I) The operational efficiency levels of provinces and regions are relatively different. (II) Generally, the operational efficiency of the generation stage is significantly lower than that of the transmission–distribution stage. (III) Factors with efficiencies ranked from highest to lowest are capital, labor, and energy. (IV) Major policy implications include breaking down the barriers to prompt interprovincial electricity transmission, developing the energy storage industry, implementing the flexibility transformation of thermal power units, and reducing the cross subsidy to the residual electricity consumption. [Display omitted] • A dynamic network structure is used for operational efficiency analysis. • Extended methods are designed to measure the factor efficiency and the group gap. • The model is applied to China's electric power industry. • Key policy directions to improve the operational efficiency are recognized. [ABSTRACT FROM AUTHOR]

Abstract: The SG (smart grid) is a modernized and a future-oriented electric grid that deals with the whole energy chain, from generation to consumer. Among the SG applications, DR (demand response) is an important control mechanism to manage the electricity consumption of the customer in response to supply conditions. In buildings, DR is managed through installed communication networks which support DR applications. BACnet is an international standard communication protocol for building automation and control systems. BACnet uses ZigBee as a wireless communication protocol. Both BACnet and ZigBee have their own DR applications. In this study, we developed a BACnet-ZigBee gateway that maps the DR application of BACnet to that of ZigBee and vice versa. In addition, we developed an experimental facility to demonstrate how the BACnet-ZigBee gateway can be implemented for DR applications in buildings. We also measured the communication delay to verify that the BZ-GW (BACnet-ZigBee smart grid gateway) developed here satisfies the requirements of real-time DR service in buildings. [Copyright &y& Elsevier]

Abstract: The energy-saving generation dispatch (ESGD) policy released by Chinese Government in 2007 is a new code for optimally dispatching electric power generation portfolio in the country with the dual objectives of improving energy efficiency and reducing environmental pollution. The ESGD is substantially different from the competitive market in the developed economies, the traditional economic dispatching or the rational dispatching principle implemented in China prior to the new policy. This paper develops a hybrid method that integrates the extended priority list (EPL), the equal incremental principle (EIP) and a heuristic method to optimize daily generation schedules under ESGD. The EPL is presented to search desirable units set that satisfies the complicated duration period requirements based on thermal unit generation priority list. The EIP is developed to allocate load among the committed units within the combined set. A heuristic method is proposed to deal with inequality constraints, which usually result in difficulty for power allocation, and used to improve these results. The algorithm has been embedded into a newly developed decision support system that is currently being used by operators of the Guizhou Province Power Grid to make day-ahead quarter-hourly generation schedules. [Copyright &y& Elsevier]

Many empirical studies have examined the causal relationship between electricity consumption and economic growth. Given the significant role of China in global economic growth and electricity use, this study analyzes the multiple causal mechanisms underlying the electricity consumption-economic growth nexus in China using a structural equation model and comparing the nexus with national-level data from 1978 to 2019. This study contributes to the understanding of the direct and indirect causal mechanisms between the electricity consumption-economic growth nexus. We find: (1) the nexus has strengthened with time, and the direct and indirect effects from electricity consumption to economic growth have also increased with time; (2) indirect influences of electricity consumption on economic growth are stronger than the direct influence; and (3) increasing ecological awareness brought about by economic growth increases the demand for electricity generation from clean energy sources. Therefore, the Chinese government should formulate policies to facilitate high-quality electricity production to meet the increasing demand from economic growth. • SEM tests the causality between electric consumption and economic growth in China. • Causal mechanisms between electricity consumption and economic growth are compared. • The direct effect of electricity consumption on economic growth increases. • The effect of electricity development on ecological awareness increases with time. [ABSTRACT FROM AUTHOR]

The rising share of Variable Renewable Energy Sources (VRES) in the electricity generation mix leads to new challenges for the whole energy system. It especially raises technological issues to handle variability and to match electricity load with supply at all times. This study introduces a new methodology to quantify the relevance of different electricity storage technologies, based on a time scale analysis. It additionally provides an understanding of how electricity storages work in combination to handle variable load and intermittent generation. First, we set up a simple model of variable production, fluctuating over a single time-scale. This analysis provides figures of merit for electricity storage and curtailment. Second, we simulate the collaboration and competition behavior of various storages with a dual time-scale signal. Then, results are compared with the optimization of an energy system with real variable electricity supply and consumption time-series. We eventually highlight the trade-off mechanisms between the storage efficiency and its investment cost. • A parametric study is conducted on electricity storage for intermittent production. • Long-term electricity storage requires very low-cost technologies. • Short-term storage can afford higher costs if efficiency is high. • The optimal solution can involve two different storages for long and short terms. • Oversizing & curtailing the production can be more relevant than long term storage. [ABSTRACT FROM AUTHOR]

More utilities, energy providers, and governments are considering the transition to 100% renewable or carbon-free generation to satisfy electricity demand. This transition requires consideration of numerous factors including cost, resource adequacy, and geographical location, among others. Therefore, models that can explore the optimality of tradeoffs between multiple factors are crucial for planning this transition. An optimization problem formulation is proposed to analyze the amount of renewable generation and energy storage required to balance 100% of a utility's electricity demand on an hourly timescale over multiple years, while minimizing a desired cost. This formulation accounts for geographical location and accommodates regional energy trading, and it enables analysis of important metrics for planning, such as firm capacity, capacity factors, land area requirements, and amount of curtailed generation. This optimization-based approach is used to explore case studies in New Mexico, which is an area with significant potential for solar and wind generation in the United States. Considering multiple years of historical meteorological data and electricity demand data, results show that the amount of renewable generation required is an order of magnitude larger than the average demand, and that most of the generation is curtailed, which motivates a regional energy trading approach. • Optimize size of distributed energy resources for 100% renewable operation. • Optimize geographical locations and regional energy trading. • Sufficient firm capacity requires resources several times larger than demand. • Peaker plants are not needed in 100% renewable systems. • Energy trading can drastically reduce size of resources and curtailment. [ABSTRACT FROM AUTHOR]

This study explores the impacts of increased photovoltaic (PV) penetration on a real-world power grid. Detailed operational scenarios of existing distributed energy technologies, including grid-tied PV, heat pump, micro-cogeneration, and battery systems among smart residential prosumers are examined using smart meter data. Then, to quantify the grid-support interactions of the decentralized electricity generation and consumption under high grid PV penetration, we introduce grid-support indicators to recognize their induced month-to-month variations based on the dynamic grid spot trading price and carbon emission density profiles. The results show that increased electricity consumption is attractive when the flexible consumption is shifted to a low grid spot trading price and carbon emission intensity period. The grid-support benefits of distributed generators are improved when their output enables a reduction in imported electricity during the grid residual load peak period. The direct simple integration of distributed PV generation makes it difficult to achieve grid-support operation at high solar penetration levels. The analysis results provide insight into the favorable scheduling of the heat pump consumption for time periods corresponding to a large quantity of available on-site PV generation to improve the grid-support performance. The results would help policymakers govern grid-support assets more equitably from a system-level perspective. [Display omitted] • Impact analysis of rising PV penetration on the public grid based on open data. • Detailed operational scenarios of existing flexible energy systems in residential sector. • Comparative quantification of distributed technologies' grid-support performances. • Optimal combination of heat pump and PV systems improves grid-support coefficient. [ABSTRACT FROM AUTHOR]

To find a sustainable municipal solid waste (MSW) management and renewable electricity generation system is need of the hour. In modern hybrid electricity generation systems, the use of MSW on priority is not evaluated yet. To satisfy this need, a small scale MSW based microgrid is proposed and evaluated in economic and reliability terms. First, the waste of the selected city is characterized considering four seasons summer, spring, autumn and winter for a complete year. Then a microgrid system is designed using MSW processing techniques namely, anaerobic digestion (AD) and gasification that are further integrated with solar photovoltaic (PV) system, battery and the main grid. The size of the microgrid is optimized using an artificial bee colony (ABC) algorithm targeting the configuration that meets the desired system reliability requirements with minimum cost. The proposed system found as a sustainable approach to process the MSW of a small city (0.1 million population) and a reliable mean to fulfill the electricity demand of a village having 225 houses in India. The annualized system cost (ASC) of the proposed hybrid system found to be INR 5760919 with levelized cost of electricity (LCOE) 5.65 INR/kWh (0.0737 USD/kWh). The total MSW landfilling avoided cost is 451898.9 INR/year that is 17% of its processing cost. [Display omitted] • Proposed sustainable utilization of municipal solid waste for electricity generation. • Municipal solid waste based microgrid is conceptualized and optimized. • Proposed system is evaluated in economic and reliability terms. • The system avoids a significant amount of investment for landfilling of waste. [ABSTRACT FROM AUTHOR]

Abstract: South Africa has a highly centralized network, in which almost all electricity is produced in Mpumalanga and transmitted throughout South Africa. In the case of the Western Cape, electricity has to be transmitted over 800–1370 km. This generates losses and entails high transmission costs. Investments in additional production and transmission capacity are needed to cope with the growing demand. Although there is a large potential for solar energy in South Africa, investments are lacking while large investments in new coal-fired power plants are being executed. These coal power plants do not only increase the need for heavier transmission infrastructure, but also have a higher CO2 emission level and a higher pressure on water reserves. This paper performs a more comprehensive cost-analysis between solar energy production and coal production facilities, to make a more elaborate picture of which technologies are more plausible to foresee in the growing demand of electricity. The current centralized electricity infrastructure makes the investment in large production facilities more likely. However, it should be questioned if the investment in large centralized solar parks will be more beneficial than the investments by consumers in smaller solar PV facilities on site. [Copyright &y& Elsevier]

Abstract: We can get an (energy efficiency) EE improvement if we produce a flatter daily load curve, leading to a higher efficiency of the power system, making better use of the generation and transport electricity chain, thus avoiding over-investment in equipment used just few hours a year. Tariff flexibility of the (Time of Use) TOU type is one of these measures. Generally, TOU systems are designed to minimize total system cost, which may cause losses in distribution companies (DISCOs), generating opposition. On the contrary, the present paper proposes a TOU system for electricity consumption in Chile where optimal prices are obtained in order to maximize total income of DISCOs. In this manner, the proposed TOU system is, by definition, beneficial for DISCOs and it may lead to a win–win situation among DISCOs and consumers. In particular, we show that such a system, implemented in a country like Chile, would allow for DISCOs a total potential benefit of 811.7 millions of dollars for the 3-year study period (2005–2007), considering initiatives that promote a 5% savings in real consumption during on-peak hours, obtained by the spread or difference between the proposed and the current systems. [Copyright &y& Elsevier]

Abstract: Multiple energy generating cycles such as tri-generation cycles, which produce heat and cold in addition to power through burning of a primary fuel, have increasingly been used in recent decades. On the other hand, advanced exergy analysis of thermodynamic systems by splitting exergy destruction into endogenous and exogenous parts identifies internal irreversibilities of each of the components and the effect of these irreversibilities on the performance of other components. Therefore, main sources of exergy destruction in cycles can be highlighted and useful recommendations in order to improve the performance of thermodynamic cycles can be presented. In the present work, a tri-generation cycle with 100 MW power production, 70 MW heat and 9 MW cooling capacity is considered. For this tri-generation cycle, effects of various thermodynamic parameters on the amount of endogenous and exogenous exergy destructions, exergy loss and the amount of fuel consumption, are investigated. The results indicate that, increasing compressor pressure ratio, pre-heater outlet temperature and excess air leads to better combustion and lower exergy loss and fuel consumption. Increasing the mass flow rate of steam generator, while keeping the cycle outlet temperature constant and considering cooling capacity variable, lead to increase the first- and second-law efficiencies of the cycle. [Copyright &y& Elsevier]

Abstract: This paper specifies and estimates state-level models of short- and long-term electricity demand in the United States. The short-term model predicts hourly load based on weather and calendar inputs. The long-term model estimates interannual demand, and includes population, prices, and gross state product as predictors. These models are combined to incorporate the short- and long-term trends in electricity consumption when generating forecasts of diurnal patterns into the future. Finally, the authors investigate the effects of short-run price elasticities of demand. The short-term model is shown to be within 95% accuracy of actual levels in out-of-sample tests. [Copyright &y& Elsevier]

This paper assesses future electricity generation portfolios in Thailand in 2030 given uncertain future fossil-fuel prices, carbon pricing policies, electricity demand, and capital costs. Thailand faces challenges for generation investment given its rapid socio-economic progress and fast growing demand. A novel generation investment and planning decision-support tool which incorporates a Monte Carlo extension to conventional optimal generation mix methods combined with portfolio-based analysis techniques, is used. The tool can formally assess tradeoffs between expected future generation costs, cost uncertainties, and CO2 emissions for the range of different generation portfolios. Results highlight that different levels of future carbon pricing will have significant impacts on the most appropriate generation portfolios. The impact of carbon pricing, however, is not on the appropriate proportion of combined cycle gas turbines (CCGT) in the mix but, instead, on the future role of coal versus nuclear in Thailand. Compared with the current proposed 2030 generation mix, it is possible that there are other generation portfolios that offer lower expected costs, cost uncertainty, and CO2 emissions depending on future carbon pricing. Results suggest that this investment decision-support approach may have value for electric utilities and policy-makers contemplating significant generation investments under high future uncertainty and conflicting policy objectives. [ABSTRACT FROM AUTHOR]

Abstract: Distributed Energy Resources (DER) scheduling in smart grids presents a new challenge to system operators. The increase of new resources, such as storage systems and demand response programs, results in additional computational efforts for optimization problems. On the other hand, since natural resources, such as wind and sun, can only be precisely forecasted with small anticipation, short-term scheduling is especially relevant requiring a very good performance on large dimension problems. Traditional techniques such as Mixed-Integer Non-Linear Programming (MINLP) do not cope well with large scale problems. This type of problems can be appropriately addressed by metaheuristics approaches. This paper proposes a new methodology called Signaled Particle Swarm Optimization (SiPSO) to address the energy resources management problem in the scope of smart grids, with intensive use of DER. The proposed methodology''s performance is illustrated by a case study with 99 distributed generators, 208 loads, and 27 storage units. The results are compared with those obtained in other methodologies, namely MINLP, Genetic Algorithm, original Particle Swarm Optimization (PSO), Evolutionary PSO, and New PSO. SiPSO performance is superior to the other tested PSO variants, demonstrating its adequacy to solve large dimension problems which require a decision in a short period of time. [Copyright &y& Elsevier]

Abstract: Peak electric demand due to cooling load in the Desert Southwest region of the US has been an issue for the electrical energy suppliers. To address this issue, a consortium has been formed between the University of Nevada Las Vegas, Pulte Homes (home builder) and NV Energy (local utility) in order to reduce the peak load by more than 65%. The implemented strategies that were used to accomplish that goal consist of energy efficiency in homes, onsite electricity generation through roof integrated PV, direct load control, and battery storage at the substation level. The simulation models developed using building energy analysis software were validated against measured data. The electrical energy demand for the upgraded home during peak period (1:00–7:00 PM) decreased by approximately 37% and 9% compared to a code standard home of the same size, due to energy efficiency and PV generation, respectively. The total decrease in electrical demand due to energy efficiency and PV generation during the peak period is 46%. Additionally, a 2.2 °C increase in thermostat temperature from 23.9 °C to 26.1 °C between 4:00 PM and 7:00 PM has further decreased the average demand during the peak period by 69% of demand from a standard home. [Copyright &y& Elsevier]

Abstract: China''s total installed electrical power capacity reached 700GW by the end of 2007 and is predicted to surpass 900GW in 2010. The rapid increase in energy demand and increasing global warming have both pushed China to change its current electrical power structure where coal power accounts for nearly 75% of the total electric power generation. China has already become the world''s largest solar water heater producer and user. However, there is still much to be done in the solar thermal power field before its commercialization. Solar thermal power technologies including solar power towers, solar parabolic trough concentrators, solar dish/stirling systems, linear Fresnel reflectors, and solar chimneys have been studied in China since the 1980s. A 10kW dish/stirling project was funded by the Ministry of Science and Technology (MOST) during 2000–2005 with a 1MW solar power tower and research of trough concentrator metal–glass evacuated tubes supported during 2006–2010. This paper describes a continued solar thermal power development roadmap in China in 5-year intervals between 2006 and 2025. [Copyright &y& Elsevier]

Abstract: In this paper, different electricity demand scenarios for Spain are presented. Population, income per capita, energy intensity and the contribution of electricity to the total energy demand have been taken into account in the calculations. Technological role of different generation technologies, i.e. coal, nuclear, renewable, combined cycle (CC), combined heat and power (CHP) and carbon capture and storage (CCS), are examined in the form of scenarios up to 2050. Nine future scenarios corresponding to three electrical demands and three options for new capacity: minimum cost of electricity, minimum CO2 emissions and a criterion with a compromise between CO2 and cost (CO2-cost criterion) have been proposed. Calculations show reduction in CO2 emissions from 2020 to 2030, reaching a maximum CO2 emission reduction of 90% in 2050 in an efficiency scenario with CCS and renewables. The contribution of CCS from 2030 is important with percentage values of electricity production around 22–28% in 2050. The cost of electricity (COE) increases up to 25% in 2030, and then this value remains approximately constant or decreases slightly. [Copyright &y& Elsevier]

The performance of PV-integrated dynamic overhangs is evaluated when applied to windows for a prototypical home located in various US climates. The analysis includes both the shading effects on heating and cooling thermal loads as well as the electricity generated by the PV arrays. The dynamic overhangs can slide and rotate to maximize both their shading impacts and their PV generation levels. Various control strategies are investigated to operate the PV-integrated dynamic overhangs to minimize annual net energy demand for US home. A series of analyses are conducted to assess the impact of the design features of the overhangs, operation strategies, as well as the climatic conditions. The series of analyses indicate that the PV-integrated dynamic overhangs can provide significant benefits both for managing the shading effects and for generating electricity. Specifically, PV-integrated dynamic overhangs can achieve 57.8% savings in total annual energy demand for a home located in San Francisco, CA, due to the window shading effects of the overhangs and the PV electricity generation. Moreover, several design features can be optimized to enhance the performance of PV-integrated dynamic overhangs such as their geometry as well as the size and the orientation of the windows. • A novel rotating and sliding overhang is evaluated for US residential buildings. • Hourly, daily, and monthly settings are considered for the dynamic overhangs. • Optimal control strategies are identified to operate the dynamic overhangs. • The proposed dynamic overhangs can reduce by up to 50% the energy use for homes. [ABSTRACT FROM AUTHOR]

Energy production has become an important concern because excessive coal and oil use for electricity generation and consumption in developing economies threaten the global environment. Past research mainly focuses on energy consumption, while there is a scarcity of models on the energy and electricity production-environmental pollution nexus in the Environmental Kuznets Curve literature. As an initial attempt, the paper examined the simultaneous impact of electricity production and electricity consumption on carbon dioxide emissions in one of the top energy-consuming, energy-producing, and carbon-emitting nations (China). The study applied the Zivot and Andrew tests to check the stationarity of the series with structural breaks. The autoregressive distributed lag (ARDL) method was to compute long-run coefficients using two separate models: Model 1 (total electricity production and environmental pollution) and Model 2 (total electricity consumption and environmental pollution) were considered. The results showed that electricity production and electricity consumption significantly contributed to environmental pollution in the short- and long-run. Apart from validating the EKC hypothesis (inverted-U-shaped curve) in the short- and long-run, the estimation demonstrated that agriculture value-added mitigated the environmental pollution. The paper offers important policy implications. [Display omitted] • Environmental effects of electricity (production and consumption) were examined for China. • Simultaneous equation modeling framework was used. • Electricity production increased CO 2 emissions in China. • Electricity consumption increased CO 2 emissions in China. • Agriculture value added decreased CO 2 emissions in China. [ABSTRACT FROM AUTHOR]