Amount Of Electricity Consumption Research Articles

Zero energy building optimization for a residential complex with a new optimized cogeneration system for electricity, cooling, heating and freshwater production

In this research, a multi-generation system was proposed to meet the energy consumption of a 120-unit zero-energy residential complex with 100 m2 and 2 bedrooms in Sydney, Australia. To meet the heating and cooling demand of the residential complex, a compression chiller was used, and electrical energy was provided using the system's production power. The main goal of this research is to define a zero-energy building by providing the energy consumption of a residential complex with a solar system. Building Energy Optimization (BEopt) Software has been used for the process of simulating the residential complex and extracting the required load of the residential complex. The design of the load supply system of the residential complex was done using EES thermodynamic software. In this research, a large number of inputs have been used in different scenarios to optimize the residential complex, and the tool used in this research by all the modes introduced as inputs turns the problem into a multi-objective optimization problem. The results of the investigation of the residential complex showed that the amount of electricity consumption, heating, and cooling of the residential complex during the year is 36728, 133123, and 17,902 kWh, respectively. Also, by optimizing the energy consumption of the residential complex, it is possible to reduce CO2 emissions and prevent the increase in environmental pollution. The optimal results showed that the system in its most optimal state can reach an exergy efficiency of 25.18 % and a system cost rate of 46.6 $/h. The performance of the system in the city of Sydney showed that the system can produce 166,164 kWh of electricity, 467,945 kWh of heating, and 183,370 kWh of cooling in one year. Comparing the results of residential complex consumption load and system production showed that the proposed system can easily provide the residential complex consumption load throughout the year.

Application of PCM in a Zero-Energy Building and Using a CCHP System Based on Geothermal Energy in Canada and the UAE

In this research, the optimization of energy consumption of zero-energy buildings using PCMs in the two study cities of Vancouver and Dubai and its energy supply with a multi-generation geothermal system is discussed. PCMs used in the walls and roofs of designed buildings are of two types, namely PCM (solid) and PCM (liquid). By optimizing the energy consumption of the residential complex in two study cities, it is finally possible to choose the best mode in optimal conditions to reduce energy consumption in the residential complex, reduce the costs of the residential complex, and reduce the environmental pollution. The results showed that the amount of electricity consumption, heating, and cooling of the residential complex during the year in the city of Vancouver is 8493.55, 7899.1, and 1083.97 kWh, respectively, and in the city of Dubai, the values are 9572.1, 8.99, and 18,845.44 kW, respectively. Also, by optimizing the energy consumption of residential complexes in Vancouver and Dubai, it is possible to reduce CO2 emissions by 2129.7 and 2773.2 kg/year, respectively. The electricity consumption of the residential complex in Dubai is 11.26% and the carbon dioxide emission is 23.20% more. In the end, a multi-generation system is proposed to meet the energy consumption of a six-unit zero-energy residential complex with 120 m2 and two bedrooms in Vancouver, Canada. By setting up the study system in the city of Vancouver, 237,364.6 kWh of electricity, 425,959.4 kWh of heating, and 304,732.8 kWh of electricity can be produced in one year. According to the investigation, the geothermal system can easily provide the energy consumption required by residential buildings.

Audit Energi dan Rekomendasi Penghematan Energi Listrik di Gedung Rumah Sakit

The actions for electricity consumption more efficiently is reducing the amount of electricity consumption of renewable and non renewable energy for human living sustainability or we can called energy conservation. Energy audit purposed to the energy utilization evaluation progress, is still infrequently applied in Indonesia, especially in huge building. The analyzing result of A building in the Dr. Radjiman Wediodiningrat Asylum, the first value audit of Energy Use Intensity (EUI) is 21,944 kwh/m2/year it was include the criteria of efficient. This EUI value is below EUI research that was issued by ASEAN-USAID in 1992 and the EUI value average of hospital in Korea, while detailed audit obtained energy value of EUI is 23,213 kWh/m2/year is included to the criteria of efficient. The THDi value in electricity system is IR= 33,2%, IS=24,48%, IT= 34,85% and IN=122,96%, the THDi value is not complied with IEEE 519-2014 permission which is the value is ISC/IL> 1000 has restraint value by 20%. Whereas the THDv value in electricity system is VR-S= 2,04%, VS-T= 2,11%, VT-R= 1,97%, VR-N= 3,83%, VS-N= 3,69%, VT-N= 3,73% and VL-L= 2,04% and VL-N= 3,75%, the value of THDv is complied the permissiod by IEEE 519-2014 with Voltage system of identification by 0,4 kV, therefore the limit value of THDV which permitted is 8%.

FORECASTING ELECTRICITY CONSUMPTION USING NEURAL NETWORKS

The problem of early and accurate forecasting of electricity consumption is acute for the unified energy system of Ukraine. With successful forecasting of consumption, which is based on many aspects, it is possible to buy electricity/losses in different market segments much more profitably, saving large amounts of money, which can then be directed to the development and modernization of electricity networks. This has always been an urgent issue, but today, when a large part of Ukraine's energy equipment has been destroyed by Russian missiles, it has become even more painful. The use of the method of artificial neural networks (ANN) for short-term forecasting of electricity consumption is considered. It was established that ANN can be used to make a forecast of electricity consumption a day ahead with an error of 4.86% compared to the actual amount of electricity consumption. Performing a comparison of forecast values with actual values allows us to talk about the adequacy of the selected forecasting model and its application in practice for the successful operation of energy supply companies in the electricity market.

Straw density board vs. conventional density board: Is straw density board more sustainable?

The widespread use of density boards in various industries has caused a series of environmental issues. The results of this study can inform policy-making and facilitate the sustainable development of density boards. The research focuses on the comparison between 1m3 conventional density board and 1m3 straw density board, with the system boundary defined as "from cradle to grave." Their life cycles are evaluated across three stages: manufacturing, utilization, and disposal. To facilitate environmental impact comparisons, the production stage was divided into four scenarios based on different power supply techniques. To identify the environmental break-even point (e-BEP), variable parameters for transport distance and service life were incorporated into the usage phase. The disposal stage evaluated the most prevalent disposal method (100 % incineration). Regardless of the power supply method, the total environmental impact of conventional density board throughout its life cycle is always higher than that of straw density board, owing to the large amount of electricity consumption and the utilization of urea-formaldehyde (UF) resin adhesives in the raw material stage of conventional density boards. During the production stage, while the conventional manufacture of density boards results in environmental impacts ranging from 57 % to 95 %, which surpasses those of straw-based alternatives at 44 % to 75 %, modifying the power supply technique can alleviate such impacts by 1 % to 54 % and 0 % to 7 %, respectively. Thus, altering the power supply technique can effectively mitigate the ecological footprint of conventional density boards. Moreover, when assuming a service life, the remaining eight environmental impact categories exhibit an e-BEP at or prior to 50 years, with the exception of the primary energy demand (PED) values. Based on the environmental impact results, relocating the plant to a more judicious geographical location would indirectly increase the break-even transport distance and consequently mitigate the environmental impact.

Extremely randomised trees machine learning model for electricity theft detection

Electricity ranks among the world’s most plundered commodities. The fraudulent act of acquiring electrical power without paying for it is termed electricity theft. Electricity theft is captured in power distribution systems as non-technical losses (NTL), representing a major loss in revenue for power utility companies. Electricity theft has far-reaching financial consequences owing to unrealised revenue, and this has a knock-on effect on both developed and developing countries because electricity represents a major part of a country’s GDP and facilitates other industries. AMI-based smart energy meters (SM) gather large amounts of electricity consumption (EC) data that power utilities can utilise to monitor and detect fraudulent customers. This EC data is fed to a machine learning (ML) based electricity theft detection model to learn the behaviour of fraudulent customers. However, existing ML-based electricity theft detection (ETD) models do not produce the best outcomes because of; consecutive missing values in EC datasets, data class imbalance problems, inappropriate hyperparameter tuning of ML models, etc. This research introduces an ETD model using an extremely randomised trees classifier to detect electricity theft in smart grids efficiently. SMOTE Tomek sampling is used to deal with the data class imbalance, and the grid search optimisation technique is employed to optimise the hyperparameters of the proposed model. The proposed model shows its capacity to detect electricity theft by obtaining 98%, 95.06%, 98%, 97%, 98%, and 99.65% accuracy, Matthew’s correlation coefficient, detection rate, Precision, F1-score, and area under the curve receiver operating characteristic, respectively.

An energy-efficient topology-aware virtual machine placement in Cloud Datacenters: A multi-objective discrete JAYA optimization

By the advent of cloud computing and the numerous related web applications, data center networks (DCNs) are becoming complex to provide all-to-all communications between underlying devices; it is done by spending a huge amount of electricity consumption. Energy consumption management is the first class concern for cloud providers in this energy hungry devices and also for the green computing goals. In the large DCs, the power management of hundreds or even many thousands idle switches can be a promising approach toward overall cost reduction. The virtual machine placement (VMP) scheme which is aware of both VMs affinity and underlying network topology for co-hosting dependent VMs as physically near as possible and lowering down power state of idle devices can enhance sustainability objectives. On the other hand, resource wastage lowers system utilization which leads more physical server usage causing more power consumption as a consequence. To address the issue, this paper presents an energy-efficient topology-aware VM placement scheme in the cloud DCs which is formulated to a multi-objective optimization problem with power consumption and resource wastage minimization perspective. To deal with this combinatorial problem, an advanced multi-objective discrete version of JAYA (MOD-JAYA) algorithm is presented since the search space of VMP is discrete in nature. The proposed algorithm is validated by intensively variable circumstances and simulations upon conducted scenarios. The simulation results prove the superiority of the proposed advanced MOD-JAYA algorithm in solving VMP problem in comparison with other existing schemes in terms of prominent assessment metrics.

COMPARISON OF SHORT-TERM FORECASTING METHODS OF ELECTRICITY CONSUMPTION IN MICROGRIDS

Context. The current stage of development of the electric power industry is characterized by an intensive process of microgrid development and management. The feasibility of using a microgrid is determined by the fact that it has a number of advantages compared to classical methods of energy generation, transmission, and distribution. It is much easier to ensure the reliability of electricity supply within the microgrid than in large energy systems. Energy consumers in a microgrid can affect the power balancing process by regulating their loads, generating, storing, and releasing electricity. One of the main tasks of the microgrid is to provide consumers with electrical energy in a balance between its generation and consumption. This is achieved thanks to the intelligent management of the microgrid operation, which uses energy consumption forecasting data. This allows to increase the efficiency of energy infrastructure management.
 Objective. The purpose of this work is to develop short-term electricity consumption forecasting models for various types of microgrid electricity consumers, which will improve the efficiency of energy infrastructure management and reduce electricity consumption.
 Method. The SARIMA autoregressive model and the LSTM machine learning model are used to obtain forecast values of electricity consumption. AIC and BIC information criteria are used to compare autoregressive models. The accuracy of forecasting models is evaluated using MAE, RMSE, MAPE errors.
 Results. The experiments that forecast the amount of electricity consumption for the different types of consumers were conducted. Forecasting was carried out for both LSTM and AR models on formed data sets at intervals of 6 hours, 1 day, and 3 days. The forecasting results of the LSTM model met the forecasting requirements, providing better forecasting quality compared to AR models.
 Conclusions. The conducted study of electricity consumption forecasting made it possible to find universal forecasting models that meet the requirements of forecasting quality. A comparative analysis of developed time series forecasting models was performed, as a result of which the advantages of ML models over AR models were revealed. The predictive quality of the LSTM model showed the accuracy of the MAPE of forecasting electricity consumption for a private house – 0.1%, a dairy plant – 3.74%, and a gas station – 3.67%. The obtained results will allow to increase the efficiency of microgrid management, the distribution of electricity between electricity consumers to reduce the amount of energy consumption and prevent peak loads on the power grid.

Performance evaluation of a novel adjustable lampshade-type reflector (ALR) in indoor farming practice using choy sum (Brassica rapa var. parachinensis).

The retrieval of lost light energy for promoting vegetable development could be a challenge in indoor farming practice, yet little is attempted so far. In this study, the performance of a novel adjustable lampshade-type reflector (ALR) was investigated to evaluate the feasibility of applying such a device in indoor farm racks (IFR). This application targeted at reflecting stray light back to the IFR for improving the growth and quality of leafy vegetable choy sum (Brassica rapa var. parachinensis). The optimal configuration of ALR was firstly confirmed via simulations using TracePro software. The combination of an included angle at 32° and a reflective board width of 10 cm, under 12 cm of distance between the light sources and the germination tray surface, was revealed to achieve a cost-optimal reflective effect. The simulation-based ALR was subsequently custom-built for actual performance validation. It was shown to effectively produce uniform distributions of temperature, relative humidity, and photosynthetic photon flux density as well as to accumulate more photosynthetic photon energy density along the cultivation shelf. Compared with the control where no ALR was used, the fresh weight and the dry weight of choy sum shoots cultivated using an ALR were increased by up to 14% and 18%, respectively. In addition, their morphological traits were found to be more uniform. Furthermore, their total carotenoid level was enhanced by up to 45%, while the chlorophyll b level was markedly decreased. However, no statistically significant difference was found in total phenolic content and antioxidant capacity across the shelf, indicating that the ALR application led to a more uniform antioxidant-related quality of choy sum shoot. ALR application in IFR can thus effectively boost vegetable production and result in quality improvements under an identical amount of electricity consumption in indoor farming compared with ALR-free control.

Effectiveness of the Implementation of a Data Mining Algorithm-Based Peak Restriction Method

In recent years, along with the rapid development of economy and society, the quality of life of people has been greatly improved, the total amount of electricity consumption has increased rapidly, and the scale of power system has been expanding. With the rapid increase of China's electricity supply, the corresponding infrastructure construction cannot keep up with the development of electricity consumption, resulting in a huge electricity consumption gap. Especially in recent years, some regions of China are facing industrial and industrial transformation, urban re-planning and renovation, leading to the complication of the structure and operation of the power grid, as well as the problem of heavy load and overload of power-using equipment. This paper describes the concepts and principles of the data algorithm and the peak-limit method, and makes a comparison of the economic losses of eight regions before and after the optimization of the data mining (DM) algorithm through experimental analysis. The results verify the effectiveness of the algorithm in solving the peak-limit problem and prove that the algorithm is of great research significance.

Improvement of methods to ensure energy efficiency of ball mills functioning

Mining, metallurgical, enrichment and processing enterprises require a large amount of electricity consumption. Among them, hydrometallurgical and enrichment enterprises currently consume 50-60% of total costs and 5.2% of all world electricity. The main reason for this is the use of traditional ball mills. Including metallurgical, enrichment and processing enterprises consume 5-20% of the electricity produced in the world. The main way to improve the performance of ball mills is to optimize the operating modes of the electric drive and mill equipment, to ensure stable operation of the ball mill and electric motor at full load. To determine such operating modes and ensure the operation of equipment in them, modern control systems, methods for assessing the energy efficiency of ball mills and optimizing the operating modes of electrical systems are required.

Electric Vehicle Charging/Discharging Models for Estimation of Load Profile in Grid Environments

The increasing electric vehicles (EVs) and their integration with the electrical networks to achieve the charging demands has highly affected the operation of the power system. These effects are due to the increased and variable amount of electricity consumption caused by the irregular charging patterns of EVs. As the EV penetration level increases with such irregular charging patterns, the overall load profile in a network will increase resulting in load peaks. To overcome this, a well-planned charging-discharging algorithm is needed for the EV users and the power suppliers. In light of the above issues and requirements, this paper proposes a Markov Chain Monte Carlo (MCMC) simulation-based EV charging (EVC) behavior models and charging-discharging power prediction models. The proposed methods analyze the traveling behavior of EVs, and their charging patterns to make EV a flexible load. This helps in understanding the variable electricity consumption and mitigating the peak loads and load variations. Further, to assess the operation of the proposed methods, the charging-discharging power prediction is carried out for a large EV fleet based on the historical data. The power predictions are carried out for 100 days and the convergence of the proposed algorithm is targeted to be kept below 0.5% to achieve optimal power prediction. The results showed the credibility of the proposed approach in identifying the daily average charging-discharging load curves.

Probability density function forecasting of residential electric vehicles charging profile

Residential electric vehicle (REV) is an advanced technology with a rapid growth rate in transportation and electric grids. One key challenge in the operation of REVs is the necessity of the accurate, reliable, and practical forecasting method to provide accurate information of the charging profile in the look-ahead hours. In power system, in order to optimize the production and consumption as much as possible, in addition to accurately predicting the amount of electricity consumption, it is necessary for the stability of the grid to take into account the imminent probabilities. This paper presents the main principle of the probability density function forecasting approach in residential electric vehicle (REV) charging profile. To this end, an end-to-end deep learning structure is designed and integrated with kernel density estimation (KDE). The designed network is composed of four major blocks, i.e., convolutional layers to extract full spatial features, gated recurrent unit (GRU) to fully understand the temporal features as a time-efficient version of the gated deep network, an autoregressive (AR) to model the long patterns including battery type, REV type, and number of REVs and kernel density estimator block. Furthermore, to improve the learning ability of the designed network, an attention mechanism is integrated into the design network. The numerical results on the actual REVs (about 348 REVs) demonstrate the effectiveness and superiority of the proposed network through several cases and comparison with several well-known deep and shallow-based methods.

Evaluation of thermal behavior and life cycle cost analysis of greenhouses with bio-phase change materials in multiple locations

Global warming and energy crisis have forced the communities to pursue sustainable development. Agriculture and food production are one of the fields that needs high amount of energy and produce large amount of emissions each year. In order to create sustainable development in these fields, this paper attempts to add organic fatty acid ester phase change materials to the greenhouses, which are one of the major sources of food production, for the first time. Designbuilder software and Energyplus calculating engine are employed to optimize greenhouses by adding various types of phase change materials to them. As the plan of this research is applicable to any locations, eight different locations which have dry climates are selected for the study. This paper also creates a plan that can be used by developers and researchers to select the appropriate phase change materials for greenhouses according to the climate, location and season. All energy, environmental and economic aspects of the projects are analyzed closely. The results of energy analysis indicated that phase change materials can save up to 14,577 kWh electricity in greenhouses per year. The amount of saved energy is different for each type of phase change materials. Results also showed that bioPCM-Q27 can reduce considerable amount of electricity consumption compared with the other ones. In addition to electricity, adding phase change materials to greenhouses will result a significant drop in their gas consumption during cold seasons. Up to 1348 kWh natural gas energy can be reduced each year by adding phase change materials to the greenhouses. The paper also analyzed the environmental effects of the projects. The largest amount of carbon reduction is equal to 6.2 tons per year and can be even increased to 8.6 tons per year. Financial analysis of the research also made it clear that adding phase change materials to greenhouses can save up 59 % of future costs of the project.

Environmental impact and damage assessment of the natural gas pipeline: Case study of Iran

Today, oil and gas trunklines from production to consumption have become one of the challenges for governments. Due to the need to install energy-intensive equipment such as compressors, turbines and pumps along the trunkline route, a systematic study of the environmental impact of this trunkline has received less attention from researchers. However, from a social and environmental point of view, the benefits of this transfer in the face of its environmental effects have always been criticized by indigenous groups and environmentalists in the region. The purpose of this study is to evaluate and systematically analyze the environmental effects of the natural gas trunkline process in part of the sixth national gas trunkline of Iran, which is done for export to neighboring countries. The research method is life cycle assessment. After defining the goal and scope, in the second step, the flow data of the inlet and outlet gas, the amount of gas rivet, the amount of turbine emissions, the amount of electricity consumption, the amount of gas consumed and the amount of black powder entering the collection station and energy consumption calculations were performed. In the third step, the environmental effects of the system were examined from two perspectives: (1) the effects that can lead to environmental problems and (2) the damage that can be done to human health, ecosystem, and resource. These analyses were reviewed at two general levels of the system and each station and equipment. The results showed that the total annual carbon dioxide emissions of this pipeline in Ahvaz station emit 70,149 kg CO2 eq per day, which is the highest amount of emissions among all stations. This is equivalent to 30,499 liters of car fuel per year. On the other hand, Bidboland station emits 7443 kg CO2 eq per day of carbon dioxide, which is the lowest emission among all stations. Due to the environmental effect of water consumption, Ahvaz station with 39 m3 per day has the highest amount among stations and Bidboland station with 6 m3 per day has the lowest consumption.

Electromagnetic Heating for Heavy-Oil and Bitumen Recovery: Experimental, Numerical, and Pilot Studies

Summary This study provides an extensive critical review of electromagnetic heating (EMH) methods [inductive heating (IH), low-frequency heating (LFH), and high-frequency heating (HFH)] to highlight their existing challenges in enhanced heavy-oil and oil sands recovery. In general, IH is considered to be less practicable than LFH and HFH. The resistance (ohmic or conduction) heating prevails in LFH while dielectric heating prevails in HFH. Thus, the effectiveness of LFH decreases if reservoir water is overheated to generate steam. Also, the intensity of the energy released and the temperature rise in LFH are not as significant as those in HFH. LFH also fails in penetrating the media with breaks, heterogeneities, and in partially saturated media (e.g., when some oil saturation has been produced). These challenges might somewhat be remedied by HFH at the expense of reducing the electromagnetic (EM) wave penetration depth. The advantages of HFH include remote heating through a desiccated reservoir region around the EM energy source, higher intensity of the energy released and greater temperature rise, and better EM wave penetration through partially saturated media with breaks and heterogeneities. The caveat, however, is that the practical application of HFH could be more expensive than LFH. Besides, the lower depth of EM wave penetration in HFH remains a challenge. During HFH, the temperature increase occurs as a result of the induced molecular rotation in the dielectric material, in particular if the material contains more polar compounds. The polar molecules follow the EM field. This increases the internal molecular friction within the material and generates heat, leading to the rise of temperature. Because the heat generated is a function of the stored (absorbed) energy in the reservoir, the dielectric constant or the real permittivity of the reservoir should be enhanced to enhance the performance of HFH. This ensures that the temperature has risen reasonably in a reasonable amount of time with a reasonable amount of electricity consumption. However, to generate a uniform rise in temperature on a large scale away from the wellbore, the imaginary permittivity of the material should be reasonably lowered, too, for maximizing the penetration of the EM wave (while the real permittivity is an indication of the degree of polarization, the imaginary permittivity is associated with dielectric losses). Lowering the imaginary permittivity away from the wellbore helps minimize the effects of steam condensation (condensate formation retards the EM wave propagation) or delay steam condensation because the reservoir temperature is reduced during the later stages of oil production. The thermal conductivity of the formation should also be enhanced, especially away from the wellbore to generate a more uniform rise in temperature. These three reservoir improvements (enhancing real permittivity, lowering imaginary permittivity, and enhancing thermal conductivity) in an attempt to enhance EMH underpin the rationale behind proposing future optimizations of EMH, and in particular, HFH.

Productive uses of electricity at the energy-health nexus: Financial, technical and social insights from a containerized power system in Rwanda

We present a case study of successful uptake of a productive use of electricity (PUE) co-located at an off-grid clinic powered by OffGridBox in Rwanda. We develop a techno-economic analysis of the standardized, modular, and redeployable power supply technology, characterizing cost components, revenue considerations, and key challenges. We present a technical characterization of system utilization based on remote monitoring of electricity consumption, power reliability, and power quality at a PUE intervention site, estimating system reliability at 81% over the study period. Lastly, we characterize socio-economic costs and benefits from the productive user's perspective drawing on mixed-method interviews. We find that relatively low amounts of electricity consumption (10–30 kWh per month) command a high revealed willingness to pay (∼$3 per kWh) for the solar-powered displacement of diesel-based welding, significantly improving the unit economics of the deployed system. This analysis and data provides a resource model for the standardization of mini-grid hardware, performance and cost frameworks, and metrics to assess off-grid, under-grid and ultimately grid interactive distributed generation systems. These models are urgently needed to meet the UN Sustainable Development Goal SDG 7 commitment to achieve universal energy access by 2030.

Optimization of power and heat dual generation cycle of gas microturbines through economic, exergy and environmental analysis by bee algorithm

In this paper, the gas microturbine cycle is optimized in dual power and heat generation mode. This optimization includes optimizing the energy efficiency and exergy of the cycle as well as optimizing the price of electricity generated based on exergy-economic analysis and taking into account the effects of environmental pollution. The fuel used in the microturbine system is natural gas. To perform this optimization, first a thermodynamic modeling was performed for the mentioned cycle and then using the bee algorithm, the optimal point for system performance was determined by the code written in MATLAB software. The results of this study showed that using the bee algorithm, the best value of the objective function obtained for the mentioned cycle was obtained in the air-to-fuel ratio of 1.32, at which point the energy efficiency is 40.53%, the efficiency of the second law is 81.80%, the amount of entropy production was 0.0578 kW/K and the final amount of electricity consumption was 0.0733 kWh. Also, if the thermal resistance of microturbine blades can be increased, the efficiency can be increased up to 15% compared to the case with temperature limitation.

Life cycle assessment of opencast lignite mining

The life cycle phase of fossil fuel extraction is mainly considered in the life cycle assessment (LCA) when evaluating the energy production processes. It is then only one of many unit processes, which contribute to the blurring of mining-relevant results. There are few items in the literature focusing exclusively on the lignite mining phase and analysing the specific mining conditions and associated environmental impacts. The article focuses on the LCA of lignite mining processes on the basis of data coming from a Polish mine. The technology for opencast lignite mining is noted for its high production efficiency, high level of recovery and lower risk as regards the safety of workers when compared with underground mining systems. However, the need to remove large amounts of overburden to uncover the deposit contributes to a much greater degradation of the landscape. Analysing the results obtained, several key (hot spot) elements of the lignite mining operations were distinguished for modelling the environmental impact, i.e.: calorific value, the amount of electricity consumption, the manner in which waste and overburden are managed. As a result there is a high sensitivity of the final indicator to changes in these impacts.

Willingness to pay estimation of microhydro power plant user for hydrological ecosystem services in North Buton Wildlife Reserve Area

One of community empowerment programs implemented by the Ministry of Environment and Forestry is the construction of a Micro Hydro Power Plant (MHP) for communities around forest areas. One of the environmental services provided by forests is hydrological benefit in the form of water source to drive MHP turbines. The existence of the perceived benefit will trigger public awareness to participate in preserving forest functions. Rewards for benefits generated from forests can be manifested through efforts to conserve water resources jointly between the community and the party that manages the area. In this regard, this research is conducted to determine the willingness to pay of MHP users around the North Buton wildlife reserve to maintain the MHP unit and support forest resource conservation activities. This study employes the contingent valuation method (CVM) to calculate the willingness to pay value. The results show that MHP users are willing to pay IDR 2,611.- for each kWh of electric power. The willingness to pay is influenced by the amount of electricity consumption and the amount of income of the respondent. If it is calculated based on the volume of water used, the total willingness to pay for environmental water service is IDR 414.-per 1 liter per second.