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The objective of the present work is to provide a comprehensive picture of the benefits coming from a wind and wave project that would operate near the largest and the most significant islands. According to the data processed in this study, the wind power has values in the range 47.1 W/m2 (Borneo) – 1430 W/m2 (Greenland), while for the wave power a minimum value of 2.28 kW/m is noticed close to Sulawesi, compared to a maximum of 68.8 kW/m encountered close to Tasmania. As a next step, the performances of several wind and wave generators were evaluated. Thus, a wind farm may reduce the emissions with a maximum 84% in the case of Madagascar, or 88% near Iceland, while from the wave converters, only the Wave Dragon may reduce about 8% in Ireland or, on the other hand 86% in Iceland. In the final part of the work, the levelised cost of energy (LCOE) was also assessed. Thus, it was estimated that for some sites the wind systems already satisfy the EU target for the year 2025 of 0.11 USD/kWh, compared to the wave systems that still have a high electricity price being quite far from these targets.

Electric power, as an efficient and clean energy, has considerable importance in industries and human lives. Electricity price is becoming increasingly crucial for balancing electricity generation and consumption. In this study, long short-term memory (LSTM) with the differential evolution (DE) algorithm, denoted as DE–LSTM, is used for electricity price prediction. Several recent studies have adopted LSTM with considerable success in certain applications, such as text recognition and speech recognition. However, problems in the application of LSTM to solving nonlinear regression and time series problems have been encountered. DE, a novel evolutionary algorithm that effectively obtains optimal solutions, is designed to identify suitable hyperparameters for LSTM. Experiments are conducted to verify the performance of the DE–LSTM model under the electricity prices in New South Wales, Germany/Austria, and France. Results indicate that the proposed DE–LSTM model outperforms existing forecasting models in terms of forecasting accuracies.

By advancement and vogue of smart grid technologies, there is a strong attitude toward utilizing different strategies for participating in demand response (DR) programs in electricity markets. DR programs can be classified into two main categories namely incentive-based programs (IBPs) and time-based rate programs (TBRPs). In this paper, an improved incentive-based DR (IBDR) model is proposed. In our proposed IBP, the concept of elasticity is improved where it depends not only on the electricity price, but also is a function of consumption hour and customer type. In this program, the incentive value which is paid to the participating consumers is not a fix value and relates to the peak intensity of each hour. The proposed IBP can participate in both of day-ahead and intra-day electricity markets. The property of considering intra-day market enables consumers to provide maximum DR if possible. The proposed model is implemented on peak load curve of Spanish electricity market and a 200-unit residential complex. Different scenarios are considered to show effectiveness of the proposed DR model from various aspects including peak shaving as well as economic indices.

The Swedish pulp and paper industry (PPI) must increase energy efficiency to remain competitive on the global market, which has experienced entries from countries with cheaper energy and raw material supplies. Interactions among variables for energy use, production, energy management, electricity price and firm characteristics (FC), in different types of mills, i.e., pulp, paper and integrated mills, in Sweden from 2006 to 2015 indicate that correlations among the studied variables were different in different types of mills. This difference between types of mills seemed to originate partly from varying accessibility to production residue that could be used for energy. For all types of mills, variation of electricity prices did not correlate significantly with energy efficiency during the study period. The studied FC were firm's age, number of employees, number of companies in company group, net sales and profit for the year. Energy efficiency was more affected by the variables characterizing energy and production compared to the variables representing FC. This study also suggested presence of possible discrepancies between FC that were perceived as barriers to energy management towards energy efficiency, according to previous studies, and what was shown by the data combining variables representing energy use, production and FC.

We study the difference between urban and rural elasticities for electricity residential consumption in Portugal. Our sample covers the period from 1989 to 2010 and consists of five independent surveys. We adopt the pseudo-panel methodology creating 350 cohorts based on the size of the household, the type of location (rural or urban), the region, and the income quintile. Our results indicate that rural and urban populations have different behaviors. In particular, rural households are more sensitive to price changes than urban ones. This may happen because rural populations can more easily use other energy sources, for example, wood, for cooking and heating. As a consequence urban populations will be more affected by electricity price changes.

Using a panel data analysis of a newly developed sample of monthly data by state for January 2005 to December 2019, we estimate a series of error correction models for US residential electricity demand postulated to move with electricity price, natural gas price, income, and weather. Our key findings are as follows. First, the short-run own-price elasticity estimate is not statistically different from zero (p-value > 0.8). Second, the long-run own- and cross-price elasticity estimates are −0.054 (p-value = 0.000) and 0.019 (p-value = 0.000) under the double-log specification, smaller in size than the long-run own- and cross-price elasticity estimates of −0.120 (p-value = 0.000) and 0.069 (p-value = 0.000) under the linear demand specification. Third, price elasticity estimates have been shrinking in size over time. Fourth, erroneously ignoring the panel data's cross-sectional dependence tends to more than double the long-run price elasticity estimates. Fifth, mismatching the timing of price information's availability and consumption decision leads to anomalous price elasticity estimates. Finally, our new empirics' key takeaway of low price-responsiveness supports continuation of energy efficiency standards and demand-side management programs.

We conduct a fractional integration and cointegration study of several Kenyan electricity price series in order to determine whether signs of persistence or mean reversion can eventually be discovered. Such features can be considered as relevant when considering the possibilities of shocks affecting the energy market of Kenya, which has recently been subjected to major debate. We conclude that electricity prices in Kenya contain unit roots, implying permanent shocks lasting forever. Among the factors affecting electricity prices, we find oil prices and interest rates have significant positive effects on electricity, and based on the fact that all the series are I(1), long run relationships are examined by means of fractional cointegration. The recently introduced FCVAR model is implemented, with results showing that the series under study are fractionally cointegrated, with oil price shocks affecting electricity prices.

Exergy and exergoeconomics assessment tools were applied to a municipal solid waste (MSW) gasification system integrated with a combined cycle (WICC). The techno-economic feasibility of the system was studied using a typical MSW produced in southern Chile and a combined experimental-modeling approach. Results suggested that gasifier should operate between 850 °C and 950 °C, in order to produce a syngas with the required energy content (LHV>3 MJ/kg) to feed power and steam cycles. Operation at lower temperatures and equivalence ratios, reduced the exergy efficiencies (

The study introduces an economic analysis for district heating networks from 0.5 MW to 4 MW. The reference case describes a linear network with 1 MW input, 1 km pipeline length, and 2000 annual full-load hours corresponding to a linear heat density of 2 MWh input per year and meter of pipeline. Pipe diameter, connection load, fuel price, electricity price, and insulation class are investigated and the influence of linear and radial connection and the effect of the consumer distribution are evaluated. The reference case for an annuity of 5.1% p.a. and a heat price of 5.0 euro cent per kWh reveals heat distribution costs of 2.16 c/kWh for the optimum pipe diameter. For distributed heat consumers, the costs decrease to 1.99 c/kWh and for a radial network to 1.77 c/kWh. The evaluation reveals that district heating is related to diseconomies of scale for a linear network expansion at constant linear heat density and that the total costs are dominated by the capital costs. Consequently, the main requirement to minimise the heat distribution costs implies the use of the smallest technically feasible pipe diameter which refers to the maximum allowable differential pressure without inadmissible cavitation pitting.

To explore the existence of the “low-electricity price trap”, based on data of 280 cities in China from 2003 to 2015, first, we calculated the Malmquist productivity index and Malmquist-Luenberger Productivity Index, which were used to measure the growth rate of industrial total factor productivity (TFP) and industrial green total factor productivity (GTFP). Then the impacts of industrial electricity price on these metrics were investigated. The results showed that the increase in industrial electricity price would not inhibit development of enterprises by increasing energy costs, but would promote improvement of GTFP, which proved that the “low-electricity price trap” did exist. The higher the level of the industrial electricity price, the stronger the promotion effect. Additionally, these effects showed region heterogeneities, and they were observed highest in the east and lowest in the west. The mechanism analysis showed that although enhancing in industrial electricity price increased the energy cost of enterprises, it could force the enterprises to carry out technological innovation, improve technical efficiency, and ultimately improve GTFP. All the above results were supported by a series of robust tests.

The integrated energy system (IES) has received considerable attention worldwide recently, which can coordinate and optimize various types of energy. This paper proposes an optimal allocation method of IES considering flexible loads on the demand side. First, a model of price-based integrated demand response (IDR) is established, which is characterized by considering the uncertainties of aleatory and epistemic to evaluate the true contribution of IDR to IES. Then, the evidence theory and credibility level are introduced to deal with double uncertainties, and the load curve is optimized based on different electricity price schemes and interval probability. Next, combining optimal configuration and operation strategy of the equipment, a two-layer collaborative planning model is established to minimize the total cost, in where the optimal electricity price scheme is obtained by comparing their economy. Finally, the comparative analyses verify that: 1) IES reduces the total allocation cost by 3.99% than sub-supply system; 2) the cost can be further reduced by 7.81% based on IDR; 3) considering the uncertainty of IDR can reduce the risk of system operation with a slight increase in cost.

The purpose of this paper is to address the problems experienced by customers in the utilisation of electrical energy for air conditioners (ACs). According to the proposed schema in this paper, the customer will be able to evaluate the impacts of a probable electricity price spike and the outside temperature (Tout) to calculate the total expected electricity cost for an AC (TEC). The aim of this paper is to show how consumers can estimate the TEC for any temperature. In this research, a model considering two types of price spikes (PS) is developed, namely: short-duration and long-duration spikes. To evaluate and examine this model, spike durations of half hour, one hour, and one and half hour were simulated to determine TEC. This proposed schema also examines how the control system applies a pre-cooling method if there is a substantial risk of PS. The results present possible savings on the electrical energy consumption when the consumer applies this method to anticipate spike events. This model is tested considering the demand and market price curves of electricity in South Sulawesi, which were published by the Indonesian State Electricity Company (called PLN=Perusahaan Listrik Negara) and the value of Tout in the Makassar area.

This paper examines wholesale price behavior of energy and ancillary services (AS) in the day-ahead market (DAM) and real-time market (RTM) of the Midcontinent Independent System Operator (MISO), the second largest regional transmission organization in the U.S. Using hourly data for the period of 12/19/2013 to 12/30/2017, it estimates a system of 16 electricity price regressions that recognizes the interdependence of MISO’s electricity products. Its key findings are: (1) MISO’s regional DAM energy prices increase with the day-ahead forecasts of natural gas price and MISO’s regional loads, PJM’s DAM energy price at its MISO interface, and MISO’s DAM AS requirements; (2) MISO’s regional DAM energy prices decline with DAM schedules for nuclear and wind generation; (3) MISO’s regional RTM energy prices increase with, but diverge from, MISO’s regional DAM energy prices; (4) MISO’s system-average DAM AS prices increase with MISO’s system-average DAM energy price and procured amounts of AS; and (5) MISO’s system-average RTM AS prices increase with MISO’s system-average RTM energy price and system-average DAM AS prices. These findings imply that MISO can reduce its DAM and RTM prices for energy and AS by accelerating wind generation development, expanding demand-side-management, and easing inter-regional transmission congestion.

The heat demand of existing buildings can be reduced by improving the thermal properties of building envelope. Moreover, the heat medium temperature of district heating (DH) systems may also be influenced by adding insulation materials. An optimization model used to determine the optimum economic insulation thickness of walls (OEITW) of existing buildings with combined heat and power (CHP)-based DH systems was established based on energy grade and amount of energy. An existing residential building in Beijing, China, was used as the basis of the case study. The impact of insulation thickness (IT) on energy grade and amount of energy was studied. Sensitivity analysis method was used to investigate the impact of electricity price, interest rate, insulation material price and absorption heat pump price on the OEITW and minimum annual total cost (MATC). Results show that the OEITW and MATC can be obtained by using the optimization model. The heat demand and heat medium temperature can be lowered through adding insulation materials, resulting in the reduction of energy grade and amount of energy. Sensitivity analysis results show that the impact of sensitivity factors on the OEITW and MATC are different.

With the growing share of wind production, understanding its impacts on electricity price and greenhouse gas (GHG) emissions becomes increasingly relevant, especially to design better wind-supporting policies. Internal grid congestion is usually not taken into account when assessing the price impact of fluctuating wind output. Using 2006–2011 hourly data from Ontario (Canada), we establish that the impact of wind output, both on price level and marginal GHG emissions, greatly differs depending on the congestion level. Indeed, from an average of 3.3% price reduction when wind production doubles, the reduction jumps to 5.5% during uncongested hours, but is only 0.8% when congestion prevails. Similarly, avoided GHG emissions due to wind are estimated to 331.93 kilograms per megawatt-hour (kg/MWh) using all data, while for uncongested and congested hours, estimates are respectively 283.49 and 393.68 kg/MWh. These empirical estimates, being based on 2006–2011 Ontario data, cannot be generalized to other contexts. The main contribution of this paper is to underscore the importance of congestion in assessing the price and GHG impacts of wind. We also contribute by developing an approach to create clusters of data according to the congestion status and location. Finally, we compare different approaches to estimate avoided GHG emissions.

This study attempts to estimate the economic value of residential electricity use in the Republic of Korea. According to micro-economic theory, the economic benefit of residential electricity consumed is the sum of the actual residential electricity price and the additional WTP (willingness to pay) for the consumption. We apply the dichotomous choice contingent valuation method to assess the additional WTP. Moreover, the spike model is applied to dealing with the zero WTP responses. The results show that the mean additional WTP for the residential electricity is estimated to be KRW 15.38 (USD 0.01) per kWh. Given that the average price of residential electricity is KRW 103.40 (USD 0.10) per kWh at the time of the survey, the economic benefit that ensues from the residential electricity consumption is computed as KRW 118.78 (USD 0.11) per kWh. This information can be beneficially utilized in conducting an economic feasibility study for a new project related to residential electricity supply service.

Treatment of reverse osmosis desalination (ROD) brine has recently been suggested as a means to achieve negative greenhouse gas emissions via indirect ocean capture (IOC) of carbon dioxide. In this paper, a novel scheme that integrates the ROD/IOC process with combined cooling, heating, and power (CCHP) generation is proposed as a new negative emissions polygeneration system (NEPS). A mixed integer linear programming (MILP) model is then developed for the optimal synthesis and operation of such a system. The model uses a multi-period formulation to account for hourly variations in product demand and electricity price. The polygeneration system takes advantage of the flexible operation of the ROD/IOC process to operate as a Power-to-X (PtX) system, without the need for direct electricity storage in batteries. An illustrative case study is solved to demonstrate the model, and sensitivity analysis is performed to assess the effects of techno-economic uncertainties on system performance.

With the popularity of plug-in electric vehicles, V2G reserve will inevitably impact on the traditional reserve trading model. By analyzing the differences and similarities between V2G reserve and generation side reserve in terms of reliability and economy, this paper pointed out the trading features of the reserve market including V2G reserve, and analyzed the interactive relationships of the relevant reserve need, reserve supply and reserve price under the requirement of certain reliability. To achieve the minimal total cost of the purchase cost and risk cost of reserve trade, and taking reliability electricity price as measurement index, a new reserve trading model of electric companies concerning the V2G reserve was proposed. Furthermore, a specific solution method was put forward according to the sequential bidding. This new model, taking default probability of plug-in electric vehicle users and accident probability of generation side reserve into consideration, realized the optimal scheduling of the V2G reserve and generation side reserve, which provide a new way for future reserve trading model considering V2G reverse. Finally, the effectiveness and validity of the proposed trading model were illustrated by a numerical example.

This paper focuses on the investment decision to retrofit an existing supercritical pulverized coal (SCPC) unit with carbon capture and storage (CCS) technology. We establish a valuation model with discrete sequential investment decision-making based on real options theory, and we consider the following uncertainty factors: electricity price, carbon price, CCS investment cost and CO2 additional OM (2) the existing level of CCS technology and policy framework cannot support the plant owner to retrofit the existing SCPC unit with CCS; and (3) the carbon price or capture subsidy must be at a high level to control the CCS retrofit investment risk such that it is less than 5%. Our proposed model is most suitable for plant owners' CCS retrofit decisions.

The paper presents an analysis of market power in the Polish power sector. The study is carried out for the structure that was established by the last consolidation undertaken by the government in 2007, using a game theoretic model of the power generation market (the PolMark model). The model is run under five scenarios and eight cases. The scenarios distinguish between assumptions on strategic behaviour, whereas the cases distinguish assumptions on coal prices. The following measures are discussed in this study: electricity prices, production volumes, consumer and producer surpluses, dead weight welfare loss, CO 2 , SO 2 , NO x emissions and fuel supplies to power producers. The results confirm that the potential to exert market power in the Polish power generation sector may influence significantly electricity prices and production volumes. The analysis indicates that under the competitive scenario the average wholesale electricity price would be approximately 14.7% lower and the production would be 6.7% higher when compared to the reference scenario. Furthermore, apart from surplus transfer between producers and consumers, the dead weight loss was estimated at the level of 123.6 M€. This value reflects the net social loss resulting from uncompetitive market equilibrium.

The growing worldwide demand for less polluting shapes of energy have led to a renewed interest in the use of Micro Combined Heat and Power (Micro CHP) technologies in the residential sector. Micro CHP have been introduced around Iran recently, and expected to diffuse more and more. In this paper, technical and economic studies for the use of Micro CHP in the different climate zones of Iran are executed. These zones are categorized in to five; Tehran, Rasht, Bandar Abbas, Ardebil and Yazd, based on weather conditions. Later on using an economic model, both annual energy savings and percentage of system profitability in each zone are calculated as well as reduction in annual emissions. It should be mentioned that, for economic calculations, gas and electricity price are determined using a sensitivity analysis. This analysis indicated that profitability of Micro CHP systems are sensitive to energy prices, as well as hours needed for heating room in each climate zones. The analysis results show that Ardebil with 38 million RLS (with electricity buyback) annual energy saving is recognized as the best option for installing Micro CHP. On the other hand, Bandar Abbas with 2.5 million RLS annual energy saving is not suitable.

Carbon costs – either in the form of a carbon tax or through permit prices in an emissions trading scheme – would ultimately be reflected in higher electricity prices. Carbon cost “pass-through” is critical to the survival of existing coal generation assets and has been discussed widely as a measure of business impact in the electricity industry. This paper sets out in a structured way the factors that determine price pass-through and why this may differ greatly across different systems. Although the basic concept of price pass-through is simple, a clear understanding of the underlying factors is critical to developing insights on how carbon cost would impact on existing coal generation businesses. It is shown that pass-through can vary drastically if the underlying dispatch potential of generators varies significantly across alternative emissions reduction scenarios. It can also vary depending on the availability of competing cleaner forms of generation. Pass-through as a measure of business performance is, therefore, hard to generalize across different circumstances and should be interpreted carefully.

The optimisation code ACOM (Advanced Cogeneration Optimisation Model) is used with purpose of assessing influence of the district heat accumulator on the Elektrana-Toplana (EL-TO) Zagreb cogeneration plant economic performance. The plant supplies hot water for district heating, steam for industry and electric power. It is possible to achieve economic benefits by charging the accumulator during the day time, when the electricity price is high, and by releasing district heat during cheap night hours, when other parts of equipment may be shut down. The consequences of this strategy are the decrease of total annual electricity production and fuel consumption, whereby the savings of some 1.8 mil Euro/a are achieved with the reduction of CO 2 emission by about 23,000 t/a or 6.4% and SO x by about 200 t/a or 16.9%.

Several papers have dealt with methodological and application issues related to techniques for decomposing changes in environmental indicators. This paper aims to decompose changes in electricity intensity in Spanish industry and to explain the factors that contribute to these changes. Focusing on an energy intensity approach based on Divisia indices, we began by reviewing the two general parametric Divisia methods and six specific cases. In order to avoid obtaining significantly different results by using differing methods, all of them have been applied to Spanish data. Also two different disaggregation levels have been taken into consideration. Combined with electricity price analysis, the results of this paper indicate the poor contribution of structural change to substantial reductions in aggregate electricity intensity, and underline the role of innovation, development, diffusion and access to more efficient technologies as main contributors to the reduction of the energy/production ratio.

In this paper, the implications of CO 2 emission mitigation constraints in the power sector planning in Indonesia are examined using a long term integrated resource planning model. An approach is developed to assess the contributions of supply- and demand-side effects to the changes in CO 2 , SO 2 and NO x emissions from the power sector due to constraints on CO 2 emissions. The results show that while both supply- and demand-side effects would act towards the reduction of CO 2 , SO 2 and NO x emissions, the supply-side options would play the dominant role in emission mitigations from the power sector in Indonesia. The CO 2 abatement cost would increase from US$7.8 to US$9.4 per ton of CO 2 , while the electricity price would increase by 3.1 to 19.8% if the annual CO 2 emission reduction target is raised from 10 to 25%.

To date, analyses of the data from the Arizona time-of-use (TOU) electricity pricing experiment have failed to find a significant relationship between electricity usage and electricity price. Results indicated that if the TOU rates had an effect on usage, the effect was virtually identical for each TOU rate. In this study, usage in 1976 under the TOU rates is compared to usage in 1975 under the existing (declining block) rate to determine whether or not there was an effect. It is found that peak period usage was reduced under TOU rates by 7–16% after adjusting for differences in weather between the two years. To a lesser extent, customers reduced their usage during the intermediate period and shifted some consumption to the base period. Total consumption appears to have decreased slightly. It is emphasized that these effects of the TOU rates cannot be discovered in demand analyses that do not utilize the baseline year data.