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ObjectivesIt is important to improve the processing ability of uncertain parameters in power planning.MethodsA robust power planning model based on cardinality constrained uncertainty set under carbon trading condition was proposed. To reduce the degree of uncertainty, the uncertainty scenario set with cardinality constraints was constructed, and then the concept of “uncertainty budget” was introduced to redefine the best and worst scenarios. Based on duality theory, the robust model with max-min-max three-level programming structure was transformed into equivalent mixed-integer linear programming, which was solved by Baron solver in GAMS.ResultsThe cost of carbon trading can promote a lower proportion of thermal power generation and a higher proportion of renewable energy generation. When the uncertainty budget is larger, the objective function value is lower, and the decision-making style is more conservative.ConclusionsThe power planning strategy based on robust optimization under carbon trading conditions can improve the ability to handle uncertain parameters. The planning results are robust and feasible, providing decision support for decision-makers in situations of information uncertainty.

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The medium and long-term supply-demand imbalance of the power system in the context of the new power system is becoming more and more prominent due to the fluctuation and intermittency brought about by the high proportion of new energy sources connected to the grid. In this regard, a multi-objective power supply-demand balance optimization method considering the spatiotemporal correlation of source and load is proposed in this work. First, the autocorrelation and inter-correlation characteristics of source and load are analyzed. On this basis, a multi-dimensional scenario set construction method considering the spatiotemporal correlation of source and load is proposed. Then, the planning capacity of each regional power source and the system operation under each scenario are taken as the optimization variables. Renewable energy electricity curtailment, equivalent annual total cost, and inter-region transmission electricity are taken as the optimization objectives. Various constraints such as power source planning and operation, power balance, inter-region power transmission, and renewable energy power curtailment rate are considered comprehensively. The optimization method for the medium and long-term power supply and demand balance is proposed. Finally, the method is applied to Hunan Province, China to guide power planning. The results show that compared with traditional multi-dimensional correlation scene construction methods, the average probability density functions error of wind turbine output, photovoltaic output, and load constructed in this work decrease by 44.08 %, 73.64 %, and 57.54 %, respectively. It takes into account the regional, temporal, temporal autocorrelation, and inter-correlation of the source and load, and has similar characteristics to historical data. Compared with traditional planning that only considers economy, the optimization plan for power supply and demand balance in this work reduces electricity curtailment and inter-region transmission by 97.04 % and 72.71 %, respectively, balancing renewable energy consumption, economy, and regional independent balancing indicators.

Aiming at the problem of multi-point layout planning of a multi-energy power system, the output characteristics of a multi-energy power system composed of wind power generation, photovoltaic power generation, hydropower generation, traditional thermal power generation and solar thermal power generation are comprehensively analyzed. Combining power optimization planning with complex adaptive system theory, a multi-point layout planning model of multi-energy sources based on complex adaptive system theory is proposed. The model takes the minimum construction step size of each new energy source as the agent. Through the interaction between the agents and the accumulation of experience, the behavior rules are constantly changed, the installed positions of various types of power sources are adjusted, and the optimal layout scheme of various power capacities of each node is obtained. Moreover, an agent modeling method based on a simple rule emerging complex phenomena is proposed, which reveals the core idea of complex adaptive system theory-adaptability makes complexity. Taking an actual power grid in a certain region of China as an example, it is verified that the proposed method has a significant effect on improving the consumption of new energy, and has certain guiding significance for the actual engineering construction.

In this study, we introduce the power planning architecture design for the implementation of Nanomagnetic Logic (NML) devices along with the proposed multi-phase local on-chip graphene integration and the clock design. To efficiently implement NML-based Integrated Chips augmenting CMOS toward rebooting computing, the major considerations are: on-board magnetic field (B-field) generation capacity, power planning, smart clustering, on-chip clock design, integration and its area occupancy. By performing Ansys Maxwell electromagnetic simulations, OOMMF micromagnetic simulations and theoretical modeling, we calculated the B-field generated by the sub-60 nm graphene wire and a group of such individual wires are further explored to propose the power planning architecture, design methodology. The proposed design along with the smart clustering enhances attenuation free data propagation as well as a reduction in the requirement of on-board resources to generate B-field for data propagation and computation in resource constrained applications. The proposed power planning architecture alongside multi-phase local on-chip graphene clock integration yields area efficient design for B-field generation to propagate data among nanomagnets for arithmetic computation. Power planning architecture design alongside parallel graphene fringe and its integration with nanomagnetic logic devices including detailed B-field and ohmic loss analysis [ABSTRACT FROM AUTHOR]

This paper presents a methodology for assessing the impact of electric vehicles (EVs) on the power transmission grid of the Costa Rica Power System. The methodology considers penetration scenarios, user preferences, charging habits, and expected fleet growth. Using ETAP software, the study simulates power flow, demand behavior, and voltage levels in the presence of high penetration of electric vehicles. The analysis covers a 15-year horizon and focuses on voltage and demand profiles in 2025, 2030, and 2040. The results indicate a decline in voltage profiles that reaches dangerous levels after 2030, primarily in the distribution grid, and an increase in demand by Image 1 for 2040 in the most severe scenario. The analysis also reveals several key findings (a) the identification of problems in the electrical infrastructure starting in 2030 and a major insufficiency in accommodating the increase in EVs by 2040; (b) the need to evaluate stability in transmission grids considering loadability and voltage; (c) the necessity of investing in electrical infrastructure, driven by public policies, to meet future energy requirements and strengthen transmission networks; (d) the significance of accounting for both EV growth and electric infrastructure improvements in system analysis; and (e) the anticipation that the system's performance will fall within the extreme demand values presented in the analysis. The study emphasizes the importance of considering a broader range of scenarios and variability in parameters, especially user charging behaviors, to enable decision-makers to plan for the challenges and opportunities associated with the widespread adoption of EVs in a country's power grid.

Considering the low environmental cost and good social benefits of clean energy power generation, we propose the concept of environmental-friendly model. We also describe power expansion planning model in detail, that helps to incorporate a variety of power generating units, including solar-power, and hydropower, and which appropriately addressed the restrictions of the energy market environment. Taking into account the constraints of cascade hydropower and the uncertainty of solar power, multiple objectives of economic efficiency, load tracking coefficient, and accommodation degree of solar power are formulated. An optimal scheduling model for hybrid solar-hydro power generation system in demand side is established. This model is solved by using an improved genetic algorithm. In various seasons, weather conditions, and time intervals, simulation examples are used to identify the optimal output of the source side and the minimum load transfer volume, as well as the validity and correctness of the suggested method. By means of comparing and analyzing the reliability indices, the impact of solar-hydro power on distribution system is researched and the results show that interconnection of solar power with hydro power can play a certain role in the improvement of power system reliability. [ABSTRACT FROM AUTHOR]

Favelas in São Paulo, Brazil have been undergoing major transformations since the 1980s with the rise of upgrading programs. These programs are widely seen as ways of alleviating urban vulnerability. However, the fact that they change the political structure of favelas, causing power imbalances, goes often untold. This article discusses the outcomes of upgrading efforts in Favela do Sapé, placing a special emphasis on the social actors involved in the upgrading. Characters such as favela dwellers, governments, and parallel powers are assessed through a power planning lens. The present analysis also focuses on the social actors’ relational possibilities that are aimed at changing the power scenarios of favelas.

Given its widespread potential and technological advancements, it is critical to use the greatest amount of wind power in reaching the 100% renewable energy target in power grids. SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis is frequently used to evaluate alternative strategies in multi-criteria decision issues. This paper employs a hybrid Fuzzy Analytic Network Process (FANP) approach based on SWOT to assess the future development of wind power capacities in Turkey in light of the sectoral effects of the Covid-19 outbreak in 2020. To validate the proposed approach, the results are compared to the results of the SWOT-based Analytic Network Process (ANP), Analytic Hierarchy Process (AHP), and fuzzy AHP (FAHP). According to the findings, "Development of domestic and efficient technologies (ST1)" and "Sustaining support mechanisms in investments and technological research (SO2)" are the best alternative strategies in all analysis models. While the priority ranks of other alternative strategies are the same in AHP and FAHP hierarchical techniques, FANP differs from ANP when the linguistic assessment process is taken into account. This study proposes long-term strategies for increasing wind power capacity and sector sustainability, and it demonstrates that FANP could be an appropriate approach for prioritizing these strategies in current scenarios. [ABSTRACT FROM AUTHOR]

The medium and long-term supply-demand imbalance of the power system in the context of the new power system is becoming more and more prominent due to the fluctuation and intermittency brought about by the high proportion of new energy sources connected to the grid. In this regard, a multi-objective power supply-demand balance optimization method considering the spatiotemporal correlation of source and load is proposed in this work. First, the autocorrelation and inter-correlation characteristics of source and load are analyzed. On this basis, a multi-dimensional scenario set construction method considering the spatiotemporal correlation of source and load is proposed. Then, the planning capacity of each regional power source and the system operation under each scenario are taken as the optimization variables. Renewable energy electricity curtailment, equivalent annual total cost, and inter-region transmission electricity are taken as the optimization objectives. Various constraints such as power source planning and operation, power balance, inter-region power transmission, and renewable energy power curtailment rate are considered comprehensively. The optimization method for the medium and long-term power supply and demand balance is proposed. Finally, the method is applied to Hunan Province, China to guide power planning. The results show that compared with traditional multi-dimensional correlation scene construction methods, the average probability density functions error of wind turbine output, photovoltaic output, and load constructed in this work decrease by 44.08 %, 73.64 %, and 57.54 %, respectively. It takes into account the regional, temporal, temporal autocorrelation, and inter-correlation of the source and load, and has similar characteristics to historical data. Compared with traditional planning that only considers economy, the optimization plan for power supply and demand balance in this work reduces electricity curtailment and inter-region transmission by 97.04 % and 72.71 %, respectively, balancing renewable energy consumption, economy, and regional independent balancing indicators. • A multidimensional scene set construction method that considers the spatiotemporal correlation of source and loads is proposed. The method takes into account the temporal, regional, and temporal autocorrelation of source and loads, and the inter-correlation between different sources and loads in different regions. The constructed scene sets have similar characteristics to historical data. • A multi-objective model for optimizing the medium- and long-term electricity supply and demand balance is developed. The model takes into account the Abandoned electricity of renewable energy, the total equivalent annual cost, and the inter-region transmission electricity. • A medium and long-term power supply and demand balance multi-objective optimization method considering the spatiotemporal correlation of source and load is proposed. The method reduces the investment, operation, and environmental costs of power sources while satisfying the premise of power supply preservation, and also reduces the system's power abandonment and inter-region transmission, which promotes the local consumption of renewable energy. • The method of this work is applied to guide the optimization of power supply and demand balance in Hunan Province, China. The effectiveness of the multidimensional scenario set construction method is verified. The superiority of the multi-objective power supply and demand balance optimization method is also verified. [ABSTRACT FROM AUTHOR]

Summary: The intermittent, random, and uncertain characteristics of the energy harvested from solar or wind are threatening the commercial efficiency and reliability of power system. With the continuous expansion of renewable energy installation scale, it has become a new challenge to improve the level of renewable energy accommodation and reduce the rate of renewable energy abandonment. Power system production simulation provides basis for the planning and development of renewable energy to assess the capacity of renewable energy accommodation in the power grid accurately. Traditional power system production simulation ignores sequential variation characteristics of load and new energy power output, thus bringing non‐negligible errors. Moreover, most reported studies ignore the impact of power source location on the level of renewable energy accommodation. This paper is therefore prepared with the objective to establish a time‐series production simulation model of multi‐energy power system and carry out multipoint layout planning and design of multi‐energy power supply. To achieve this objective, extensive literature reviews are conducted with more attention given to recent studies on the field. On the basis of the traditional production simulation model, this paper adds the power flow constraints of the transmission network, and proposes a time‐series production simulation model. A multipoint layout planning and design method for multi‐energy power systems is proposed to give full play to the synergistic effects of natural resources, to achieve optimal resource allocation, and to provide a theoretical basis for improving the consumption of wind and renewable energy. The method is simulated with the actual meteorological and operation data of a province power grid in Northwest China. The simulation results of different scenarios are compared and analyzed, and the effectiveness of the method is verified. [ABSTRACT FROM AUTHOR]

Vigorously developing new energy is an important measure to deal with the energy crisis and environmental degradation, but the volatility and randomness of its output have brought great challenges to the new energy consumption. In this paper, the maximum new energy consumption is taken as the objective function, and the power flow constraints, multi-energy source capacity constraints and electrical constraints are taken as the constraints. Based on the complex adaptive system theory, the minimum construction step size of new energy sources is taken as the agent for modeling, focusing on the coordination among multiple sources, adjusting the installed node position and capacities of various types of power sources to obtain the optimal layout of power sources for multi-energy power systems. Based on the actual meteorological and operational data of a regional power grid in Northwest China, the original power system is re-planned. The simulation results verify the effectiveness of the proposed planning method.

In view of the problems such as high carbon emissions and low return on investment in conventional power planning, this paper proposed a low-carbon thermal energy power planning model based on a green certificate allocation mechanism. First, considering the uneven distribution of power generation resources in the power sector and the significant differences in power generation technologies in various regions of China, a green certificate allocation mechanism was established based on regional comparison to ensure a fair allocation of carbon quotas; then, based on the idea of full life cycle, an in-depth and comprehensive analysis was conducted on the low-carbon thermal energy power technologies, and a new low-carbon thermal energy power planning mode was established; on this basis, with investment costs, operation and maintenance costs, power generation costs and low-carbon benefits as the objective functions, a low-carbon power planning model with the maximum net benefit was established; after that, the proposed low-carbon thermal energy power planning model was optimized and solved with the discrete bacterial colony chemotaxis algorithm, and the results were compared with the optimized results of conventional power planning, and the roles of various low-carbon power elements in different low-carbon scenarios were analyzed. The results of the example analysis show that the proposed model can effectively reduce the carbon emissions of the power sector and increase the power generation benefits of the units, so it will provide a useful reference for the low-carbon power planning work in the future. [ABSTRACT FROM AUTHOR]

Adverse effects of conventional fossil fuel based power generation create an urge for clean technologies. Technologies related to the advancement of renewable energy and clean technologies are growing very fast. Development in renewable energy technologies, reliability and environmental restrictions generates the requirement of a Hybrid Power System (HPS). Cost is an essential factor which plays important role while planning any system. This factor can be included in planning an HPS via Pinch Analysis through the concept of prioritization. This concept is a well-established technique in Process Integration for designing cost-optimal resource conservation networks for various resources such as thermal utilities, water and carbon. However, its application towards the cost-optimal planning of an HPS is still to be explored. In this paper, a methodology is developed to set cost optimal targets in HPS. The proposed methodology extends the concept of prioritization towards planning of cost-optimal HPS. The capital cost of power generation depends on the type of power resource. The proposed method incorporates entry and exit on the time horizon of each power resource. The methodology presented in this paper calculates the power rating of power resources in an HPS required for satisfying the power demands with an objective to minimize the overall cost of electricity. The methodology is illustrated via two examples where 7.75% and 8.8% cost reduction potential are estimated.

Rural electrification plans are a means of enhancing electricity services in a given area within a given period of time in accordance with pre-defined strategies. Establishment of prioritization concepts on the basis of objective criteria provides important indications for practical energy development. The choice of the most suitable electrification option with the given context is vital in energy planning. In developing countries, it’s difficult to electrify a sparsely populated community. Lack of scientific and methodological know-how in regards planning, site selection, distribution and density of population settlement, economic level, and distance from the national grid are main factors to lowest electrification rates in Ethiopia. Therefore, the objective of this study is to study the introduction of renewable energy systems as a possible alternative for rural electrification by using geospatial technologies in South Gondar Zone. Data like sunshine hour/irradiance, wind speed, slope, land use-land cover, protected areas, water bodies, forest, and towns are collected and analyzed with ArcGIS. These data are used to identify optimal electrification options for particular areas in four districts of the Zone. The weighting factors of these criteria are also determined using Analytic Hierarchy Process (AHP). This weighted and reclassified values are multiplied to produce the final map of electrification options. From the final results obtained, study area covers 5226.289 square kilometers from which 2897.742, 2836.134, and 802.003 square kilometer area is suitable for electrification using solar, wind, and grid extension, respectively. Solar energy is the optimal options for most of area in the Zone.

Rural electrification plans are a means of enhancing electricity services in a given area within a given period of time in accordance with pre-defined strategies. Establishment of prioritization concepts on the basis of objective criteria provides important indications for practical energy development. The choice of the most suitable electrification option with the given context is vital in energy planning. In developing countries, it's difficult to electrify a sparsely populated community. Lack of scientific and methodological know-how in regards planning, site selection, distribution and density of population settlement, economic level, and distance from the national grid are main factors to lowest electrification rates in Ethiopia. Therefore, the objective of this study is to study the introduction of renewable energy systems as a possible alternative for rural electrification by using geospatial technologies in South Gondar Zone. Data like sunshine hour/irradiance, wind speed, slope, land use-land cover, protected areas, water bodies, forest, and towns are collected and analyzed with ArcGIS. These data are used to identify optimal electrification options for particular areas in four districts of the Zone. The weighting factors of these criteria are also determined using Analytic Hierarchy Process (AHP). This weighted and reclassified values are multiplied to produce the final map of electrification options. From the final results obtained, study area covers 5226.289 square kilometers from which 2897.742, 2836.134, and 802.003 square kilometer area is suitable for electrification using solar, wind, and grid extension, respectively. Solar energy is the optimal options for most of area in the Zone. [ABSTRACT FROM AUTHOR]

Abstract The advantage of accurate forecasts is that it solves the main problem related to renewable energies: their variability. Indeed, while renewable energies has not yet replaced fossil fuels, in spite of the efforts of many governments, it is because of their intermittent nature, hence the importance of prediction in this field. The new approach for energy prediction that we propose in this paper, is founded on the analysis of the dynamical behavior of the photovoltaic production of the Faculty of Sciences and Technology of Beni Mellal, Morocco. It consists in performing the phase space reconstruction, which allowed us later to build a database for the input of the neural network and thus take into account the dynamics of the system in the forecasting process. Then, in search of more precision, we introduce the wavelet transformation, to simplify the database constructed from phase space reconstruction. Finally, comparing between the predictions and the actual observations confirmed the efficiency of our approach. Highlights • PV production forecast has an major role in its integration into electricity mix and thus in the fight against climate change. • In this work, we propose a new approach for forecasting, based on the exploitation of the phase space reconstruction. • The proposed model is a hybrid method combining the phase space reconstruction, DWT method and recurrent neural network. • The method is tested on real observations to confirm the efficiency of our approach. • Comparison between predictions and actual data confirmed the effectiveness of our approach given the improved results. [ABSTRACT FROM AUTHOR]

With the development of smart grids and advanced technologies, the application of industrial product-service system (IPS2) in power systems become possible. The power grid company has changed from a pure power supplier to a comprehensive energy service provider providing energy storage, electric vehicle rental and electric heating. IPS2 is an integrated system, involving quantification, quality of service, flexibility and other processes. Therefore, how to establish the evaluation indicator system of smart grid is of great significance to the construction and operation of smart grid. This paper proposes an indicator-based smart grid service evaluation system from four aspects of safety, serviceability, sustainability and intelligence. In addition, a comprehensive evaluation model is established by using analytic hierarchy process and fuzzy evaluation method. It not only provides different calculation processes, but also provides different case studies from smart grids in different cities. The model is described and can be used as a decision support system for smart grid planning. [ABSTRACT FROM AUTHOR]

Abstract This Case Study considers generation capacity expansion planning in Bosnia and Herzegovina (BiH) including a range of issues related to renewable, energy efficiency, local emission and carbon reduction commitment policies. In addition, our analysis considers an outlook of wholesale electricity prices in the region. We have also compared our findings with the country's current official plan ("Indicative Plan"), which includes development of as many as four additional lignite-based plants. Our findings suggest that BiH, as a low-cost producer in the region, has significant opportunities to modernize and expand its current generation system with nearly €3 billion in new investments. This investment level is much lower than the €5 billion investment program promulgated in the Indicative Plan. These differences primarily have to do with the treatment of lignite plants. Since the end of the war roughly twenty years ago, the country has progressed quickly. Aspirations to join the EU compose a significant part of the authorizing environment for the power sector. Highlights • This Case Study considers generation capacity expansion planning in Bosnia and Herzegovina. • Our analysis considers an outlook of wholesale electricity prices in the region. • We have also compared our findings with the country's current official plan. [ABSTRACT FROM AUTHOR]

This paper presents a methodology for assessing the impact of electric vehicles (EVs) on the power transmission grid of the Costa Rica Power System. The methodology considers penetration scenarios, user preferences, charging habits, and expected fleet growth. Using ETAP software, the study simulates power flow, demand behavior, and voltage levels in the presence of high penetration of electric vehicles. The analysis covers a 15-year horizon and focuses on voltage and demand profiles in 2025, 2030, and 2040. The results indicate a decline in voltage profiles that reaches dangerous levels after 2030, primarily in the distribution grid, and an increase in demand by Image 1 for 2040 in the most severe scenario. The analysis also reveals several key findings (a) the identification of problems in the electrical infrastructure starting in 2030 and a major insufficiency in accommodating the increase in EVs by 2040; (b) the need to evaluate stability in transmission grids considering loadability and voltage; (c) the necessity of investing in electrical infrastructure, driven by public policies, to meet future energy requirements and strengthen transmission networks; (d) the significance of accounting for both EV growth and electric infrastructure improvements in system analysis; and (e) the anticipation that the system's performance will fall within the extreme demand values presented in the analysis. The study emphasizes the importance of considering a broader range of scenarios and variability in parameters, especially user charging behaviors, to enable decision-makers to plan for the challenges and opportunities associated with the widespread adoption of EVs in a country's power grid., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Author(s).)

Favelas in São Paulo, Brazil have been undergoing major transformations since the 1980s with the rise of upgrading programs. These programs are widely seen as ways of alleviating urban vulnerability. However, the fact that they change the political structure of favelas, causing power imbalances, goes often untold. This article discusses the outcomes of upgrading efforts in Favela do Sapé, placing a special emphasis on the social actors involved in the upgrading. Characters such as favela dwellers, governments, and parallel powers are assessed through a power planning lens. The present analysis also focuses on the social actors' relational possibilities that are aimed at changing the power scenarios of favelas. [ABSTRACT FROM AUTHOR]

The negative externalities apart from carbon emissions are often neglected in most power generation planning models, which will affect the human health, biodiversity, crop yield and land use greatly. To achieve a sustainable development of China’s power industry, this paper develops a deterministic linear programming model with consideration of the non-carbon externalities. This model has been applied for the case study of China for the period from 2015 to 2030, through which some interesting results have been drawn. Firstly, most of the new capacity additions are from the non-fossil fuel power plants in this planning horizon, which account for 84% of the total new capacity additions. Secondly, the power generation priority would better be given to the non-fossil fuel power plants in this horizon under the cost-effectiveness criteria. Thirdly, the minimum total cost of China’s power planning is 34.48 trillion yuan, which equals to 2% of China’s GDP during the planning horizon. Finally, neglecting of non-carbon externalities does have a significant influence on the power planning results, which will lead to a higher power generation share of technology with bigger negative externalities. [ABSTRACT FROM AUTHOR]

The UN's Sustainable Energy For All initiative has made universal access to energy by 2030 a key target. Countries wherein budgets are constrained and institutions stressed are faced with the challenge of further extending energy services – and doing so significantly. To meet this goal for the power sector in a cost-effective way, governments have to consider the deployment of a mix of stand-alone, mini-grid and grid-based solutions. To help inform analysis, planning and the decision process, this paper presents a simple, transparent, least-cost model for the electrification of rural areas. The approach builds on four key parameters, namely: (i) target level and quality of energy access, (ii) population density, (iii) local grid connection characteristics and (iv) local energy resources availability and technology cost. From an application perspective, this work can be used both for (1) fast assessments of specific energy access projects, and (2) to inform more complex regional studies using a geo-referencing software to analyze the results. Such applications are presented in the results using country case studies developed for Nigeria and Ethiopia. These show how the strategy for expanding energy access may vary significantly both between and within given regions of energy-poor countries. [ABSTRACT FROM AUTHOR]

Although the problem of rational power generation has been extensively studied, traditional approaches for power optimization do not offer good solutions to this purpose, especially in a competitive electricity market environment where many factors are uncertain. In this paper, within the framework of two‐stage linear stochastic programming, the method for power planning has been developed, with uncertain factors taken into account, through a continuously distributed set of scenarios. The objective is to find the structure of the power plants capacity in the region which minimizes the sum of the investment and the expected operating costs over the long‐term planning horizon, taking into account the environmental impact. The structure of the considered task corresponds to a power investment planning problem that often arises in the developing regions. The method is developed for solving the stochastic optimization problem by the sequence of Monte‐Carlo sampling estimators. The procedures developed make it possible to solve stochastic problems with an admissible accuracy by means of an acceptable amount of computations. As follows from numerical experiments the approach presented enables us to decrease the total expected costs of power planning versus deterministic planning solution. Santrauka Nors elektros energijos infrastruktūros ir gamybos uždavinys sprendžiamas intensyviai, tradiciniai optimizavimo metodai nepateikia tinkamų sprendinių, ypač kai elektros energijos rinkoje daugelis veiksnių yra neapibrėžti. Šiame straipsnyje pateikiamas elektros energijos gamybos planavimo metodas, sukurtas remiantis dviejų etapų stochastiniu programavimu, kai neapibrežtumas aprašomas tolydžiaisiais pasiskirstymo scenarijais. Uždavinio tikslas – rasti tinkama regiono elektros jėgainiu struktūra, kuri minimizuotų investavimo ir ilgalaikės energijos gamybos sąnaudas. Sprendžiant uždavinį atsižvelgiama į gamtosaugos problemas. Taikant optimizavimo metodą naudojami baigtinių Monte Karlo sekų įverčiai. Siūloma procedūra leidžia išspręsti stochastinius uždavinius gana tiksliai, naudojant priimtinus skaičiavimo išteklius. Skaitiniai eksperimentai rodo, kad siūlomas metodas padeda sumažinti bendrasias elektros energijos gamybos sąnaudas, palyginti su deterministiniu uždavinio sprendiniu. First published online: 10 Feb 2011 Reikšminiai žodžiai: stochastinis programavimas, Monte Karlo metodas, elektros energijos gamyba, stochastinis gradientas, statistiniai kriterijai, ϵ leistinoji kryptis

Abstract: The effective rural electrification method varies with economic status and geographical location, and the benefits of decentralized generation differ for each energy system depending on its characteristics. This paper evaluates the most effective generation strategies with rural electrification in an optimized power system of Papua New Guinea (PNG) using a linear programming model. The energy system model developed for the study includes decentralized generation, centralized generation, and grid systems of electricity and gas with consideration for the current energy system and infrastructure. Two methods of rural electrification, decentralized generation and grid extension, are compared with and without the Clean Development Mechanism (CDM). The results of simulations show that extending the power grid that allows economical generation such as coal-fired power and hydropower to supply rural electricity is a more cost effective way for rural electrification. Although grid extension is more capital intensive than decentralized generation, the former reduces the total system cost through reduction of the fossil fuel use. Extending the power grid is also effective at attracting CDM investments, since it makes the power system flexible and provides opportunities to advance low emitting energy such as hydropower. [Copyright &y& Elsevier]

Abstract: This paper presents the results of a study that estimated the DSM energy saving and load management potential in commercial and government/institutional sectors in Oman (central grid area) and evaluated its impact on generation capacity and energy savings. The end-use (lighting and air-conditioning) energy consumption data have been collected in two major segments of the commercial sector for simplicity and to save time and money. Another unique aspect of the study is the inclusion of the energy savings, in transmission and distribution (T&D) losses that are estimated by using generation expansion planning approach. The study has found that DSM is financially beneficial from customers’ point of view as the discounted payback period of investment in efficient lighting and air-conditioning is between 4 and 12 years of the surveyed sample. From the utility point of view the capacity saving at the horizon year is between 372 and 596MW and the overall energy saving for the whole planning horizon is about 29–44TWh. The total avoided cost in generation and capacity saving is somewhere between 416 and 597 million dollars. [Copyright &y& Elsevier]

Abstract: The electricity tariffs in Oman are subsidized and are based on a cost accounting approach and do not reflect the true cost incurred in generating, transmitting and distributing a kilowatt-hour of electricity at the consumer end. This paper presents the electricity tariff based on the estimation of long-run marginal cost at generation, 33kV, and 415 voltage level for Ministry of Housing, Electricity & Water (MHEW) interconnected power system of Oman. The result shows that at the generation level a marginal kW costs US$ 75per year and a marginal kWh costs 2.07¢/kWh. These costs increase as we move downstream from generation to consumer end. The average cost of electricity at the consumer end connected at 415V is 6.52¢/kWh or 25.17Bz/kWh. [Copyright &y& Elsevier]