Regional Electric Power System Research Articles

A fuzzy approach to the regional electric power system's stability monitoring based on socially available information

Subject. This article deals with the issues related to the stability of the region's electricity system. Objectives. The article aims to develop an original approach to monitoring the stability of the region's electric power system. Methods. For the study, we used a fuzzy logic approach. Results. The article proposes an algorithm for monitoring the stability of the region's electric power system based on socially accessible information, based on a fuzzy approach. The proposed forecasting research algorithm consists of five successive steps. The result of the forecasting was a polynomial function reflecting the change in the parameter of the load on the system over time. Conclusions and Relevance. The consumption indicator over time is unstable, prone to sharp changes both negatively and positively, which may be due to the specifics of the formation of demand for electricity, where the consumer's decision is of key importance. The results of the study can be used to develop strategies for regional electricity consumption systems, and can also be implemented in the practice of specific electric power enterprises as part of making forecasts for energy consumption.

A Linear Chance-Constrained Mixed-Integer Programming Model for Optimizing Regional Electric Power Systems under Carbon Constraints

In view of increasing population size and energy consumption, greenhouse gas (GHG) emissions are increasing and are one of the main causes of climate change. The regional electric power system is one of the main sources of carbon emissions, so there is an urgent need to optimize the regional electric power system to meet the Paris Agreement's long-term temperature goal. Therefore, this study provided a linear chance-constrained mixed-integer programming (LCMI) model with the objective of maximizing the total system profit and applying it to the regional electric power system. Chance-constrained programming and mixed-integer programming were integrated into the LCMI model to address input uncertainties. Including five commonly used power generation technologies, namely coal-fired, natural gas-fired, hydropower, wind power, and solar power, the model can provide the optimal electricity generation schemes and capacity expansion plans for different technologies at the regional level to meet the end-user’s needs while meeting the carbon dioxide emission targets under different risk levels. The outcomes of the research will offer decision-makers a framework for optimizing conventional regional electric power systems for their long-term sustainability in environmental and economic development.

Концептуальное представление энергетического перехода в электроэнергетике региона в новых реалиях

The energy transition is a global problem affecting environmental and energy security, sustainable economic development, social well-being. However, since its implementation differs in certain regions, it is necessary to develop specific approaches that consider local factors, external threats and risks. The article proposes a conceptual vision of energy transition at the regional level in the context of crisis, economic and logistical changes, and socio-economic, scientific and technical transformations. It is hypothesised that in order to implement the energy transition, a compensation method is required to ensure the equivalence of environmental, economic and technical results. The study analysed expert opinions published in foreign and Russian journals indexed in the SCOPUS, Science Direct, MDPI, WIT Press databases, as well as analytical reports of international energy agencies and consulting firms. The analysis revealed that the most appropriate form of organisation of the regional electric power system is a flexible model combining system and regional circuits and involving power plants with a wide range of generating capacities. The integrated power system and decentralised energy sources are seen as energy transition objects. The study presented an energy transition scheme from the implementation of low-cost energy demand management measures to the reconstruction of coal-fired power plants and construction of renewable energy facilities. An ideal model of the regional electric power system was developed as a strategic guideline in the energy transition implementation. Possible scenarios and stages of the energy transition were discussed. The findings can be applied by government and industry regulators, energy companies, large electricity consumers when planning structural, organisational and market transformations in the regional energy sector under uncertainty.

THE EVOLUTION OF THE ENERGY SYSTEMS OF UKRAINE AND THE ORGANIZATION OF MANAGEMENT ACCOUNTING IN ENERGY COMPANIES IN CONDITIONS OF RISK

An essential prerequisite for the development of Ukraine is a rationally formed energy system that covers the electricity demand of society and assures the smooth functioning of the national economy. The study presents the characteristics of different types of power plants in Ukraine, such as thermal, hydraulic, nuclear, geothermal, and tidal. Furthermore, the share of Ukraine’s contribution to the energy supply of other countries is indicated. Today, the United Energy System of Ukraine is one of Europe’s most significant energy com- plexes with seven regional electric power systems (REES), including Dnipro, Western, Crimean, Southern, Southwestern, Northern, and Central. The research presents the structure of electricity consumption by consumer groups in Ukraine. It indicates the extent of the destruction of Ukraine’s energy capacities by Russia’s invasion. The issue of developing electricity production from alternative energy sources in Ukraine is discussed. Permanent risks and the threat of destruction of energy system objects encourage managers of economic entities to carefully search for quick and accurate problem-solving tools for avoiding critical situations. The accounting system, in particular, management accounting, provides the in- formational component that satisfies the timeliness of providing boards with recommendations on making managerial decisions. The study highlights and describes the stages of implementation of management accounting in energy supply companies in Ukraine. The effectiveness of management accounting in this aspect is emphasized.

Topological structure reengineering regional electric power systems

This paper analyses the topology of a regional power system distribution grid. This research improves the efficiency of the electric power system's operation by using upgrading the redesign (reengineering) methods of topological structures within distribution grids. The research object is an electric power system that consists of generation, transmission and distribution parts and requires reengineering. The subject of research is the re-engineering of the topology of the power system distribution grid. To achieve the research purpose, modifications of k-means algorithm as well as the small step algorithm based on the statistical analysis, clustering and minimum spanning tree development methods of Prim and Kraskal are used. The modifications described in this paper allow for optimization of the network based on user needs, properties of the operating grid elements, and other additional constraints for flexibility and generality. Given the varying parameters, this method provides the means to redesign parts of the distribution grid, keeping its certain elements safe from displacement, but also the means to redesign the whole distribution grid, including changes to the number and location of transformer substations and transmission lines. Conclusions. To solve the problem of determining the territorially close groups of consumers in the paper, it was proposed to use the k-means clustering algorithm. This algorithm allows us to divide consumer sets into clusters, so that coordinates of their centres will be recommended as locations of transformer substations. The modernization of the k-means algorithm was proposed by developing procedures for adding and combining clusters using different strategies for determining starting centroids. Based on this, a method for reengineering the topological structures of regional electric power systems in terms of the possibility of their fundamental restructuring was developed. The results of this research may be useful to various enterprises, organizations or institutes dealing with the elaboration or design of electric power system development on the corporate, regional or local level.

A Multi-Stochastic SMR Siting Model Applied to the Province of Saskatchewan, Canada: Emphasis on Technological Competition and Policy Impacts

Existing electric power systems have been being faced with great concerns on climate change mitigation through the reduction of greenhouse gas emissions in recent decades. Small modular reactors (SMRs) are considered the next generation of power technologies, offering stable, safe, and environmental-friendly power. This research initially establishes a multi-stochastic SMR siting model (SSMRS) based on a dynamic mixed-integer left-hand-side chance-constrained multistage programming (DMLCM) method, aiming to provide solid bases for electric power system management to promote GHG mitigation under uncertainties and advance the practical application of SMRs in a regional electric power system. The model is applied to emission-intensive Saskatchewan power systems, which have a high reliance on fossil fuels. The commissioned coal-fired and gas-fired power plants in Saskatchewan are identified as potential sites for employing SMRs. A series of scenarios regarding technical development and carbon policies are analyzed to study the interrelationships among technological competition, policy efficiency, and system-failure risk. This is the first attempt to apply an optimization-based modeling approach for siting SMRs in Saskatchewan with detailed consideration of other new-emerging emission reduction technologies. We found that a carbon tax ranging from 30 to 40$/tonne might be reasonable in Saskatchewan after 2030 while the SMR would be partly commercially viable, occupying ∼>15% of total installed capacity. The solution obtained could provide scientific bases for decision-makers to determine optimal schemes for power supply, capacity expansions, and system costs under environmental targets, and particularly identify construction schemes for SMRs at specific sites in different periods under various risk levels.

A chance-constrained small modular reactor siting model -- a case study for the Province of Saskatchewan, Canada

In this study, a chance-constrained small modular reactor siting (CCSS) model has been first proposed for planning regional electric power systems in the Province of Saskatchewan under the pressure of greenhouse gas emission mitigation. Through incorporating three programming methods (interval linear programming, chance-constrained programming, and mixed-integer programming) in an optimization framework, the CCSS model could effectively deal with multiple uncertainties expressed as probability distribution and intervals in constraints and objectives. Since locations of retired coal-fired power stations have been proposed as new sites for small modular reactor (SMR), this model could provide the construction planning for SMR in terms of its sizes and sites. Various solutions to power generation and capacity expansion with different risk levels were obtained under the objective of minimal system costs. Results are helpful for identifying optimized strategies to increase the proportion of renewable energy under emission constraints. In addition, model without introducing the technology of SMR has been comprehensively compared with the CCSS model to recognise the effects of SMR on system costs and greenhouse gas emissions. Results indicate that the CCSS model is effective for supporting long-term clean and renewable energy utilization as well as emission reduction policy formulation in Saskatchewan.

Indices of Intermittence to Improve the Forecasting of the Voltage Sags Measured in Real Systems

This paper provides a means of forecasting the future average performance a regional electric power system in terms of sags per year based on data accumulated over a four-year period. The paper also presents statistical analyses of the measured data. The sags measured in real systems consist of both rare voltage sags and grouped voltage sags (clusters); clusters are stochastic events that must be detected and removed to allow the estimation of the future performance based on a Poisson process. To detect and remove the clusters, new site indices of intermittence are proposed. These indices were measured in every site at which clusters were present, and the measurements included the frequency, time of grouping, and the large number of sags that constituted them. The performance of the forecast was evaluated by comparing the results obtained using the proposed indices with the results derived using a time filter equal for every site. The results showed that the forecast of the rare voltage using a few years' worth of field data had acceptable errors and was not prohibitive. The main aspect, which must be investigated further, is related to the choice of the best threshold on the intermittence site indices.

A Structural Adjustment optimization model for electric-power system management under multiple Uncertainties—A case study of Urumqi city, China

In this study,a structural adjustment optimization model for electric-power system management under fuzzy-random environment was developed for regional electric power system management in Urumqi, China. The emission control policy andstructural adjustment are considered and quantified in the developed model to tackle the electric power problems of a regional electric-power system. The objective of this study was to develop a power structural adjustment optimization model with fuzzy-random parameters for discussing the effects, merits and defects of structural adjustment and policy adjustment to manage the electric power system of the city Urumqi. Finally, the results and comparisons analysis of the case study demonstrated the practicality and efficiency of the optimization method. The results indicated that the model can provide an effective linkage between conflicting economic cost and the system stability, and different power demand levels correspond to different electricity generation schemes with varied energy policy and power structural adjustment. The power generation schemes, pollution emission and CO2 emission were analyzed. The modeling results are valuable for supporting the adjustment or justification of the energy policies and structures within a complicated power system under uncertainty.

Multi-stage risk-averse operation of integrated electric power and natural gas systems

Regional electric power and natural gas systems have become increasingly interdependent in recent years, and have become subject to increasing supply and demand uncertainties associated with renewable energy sources. The present work addresses these issues by developing a multi-stage risk-averse operation model at the level of a regional integrated energy system that considers integrated demand response. Specifically, the developed multi-stage operation model can make flexible wait-and-see decisions, which are adaptive to newly observed uncertainties in the output of renewable energy sources, and the conditional value at risk approach is employed to achieve an economic operating cost at a given risk level. As a result, the multi-stage decision model outperforms conventional two-stage decision models in terms of operating costs. Test results indicate that the operation cost of the multi-stage operation model is reduced by 4.2% compared with a regular two-stage operation model. The impact of congestion in both the electricity and natural gas networks on system operations and energy prices is also investigated.

Stochastic optimization framework of the energy-water-emissions nexus for regional power system planning considering multiple uncertainty

This paper proposed a model-based framework to optimize the energy-water-emissions nexus (EWEN) for regional electric power system (EPS) planning. The framework integrated the superiority of bi-level programming (BLP), two-stage stochastic programming (TSP), mix-integer programming (MIP), interval parameter programming (IPP) models and carbon emission trading (CET) mechanism, which could effectively deal with the planning problems in the EWEN optimization process. BLP helped decision makers make leader-follower choices when they have conflicting preferences. TSP with IPP could validly address the uncertain information expressed as interval values with unknown distribution. MIP was introduced as an efficacious tool to evaluate the capacity expansion potential in EPS. CET mechanism was applied to reinforce the CO2 mitigation effect during the EWEN optimization process. Then the proposed framework was applied into a case study in Shanxi Province, a main thermal power base in China to optimize the EWEN. Results indicated that the power supply structure was inclined to show a low-carbon transition trend in the planning periods. Meanwhile, coupled with the strict water conservation policy, every 5% reduction in carbon emission density would lead to an average decreasing rate of 8% for SO2, 3% for NOx and 12% for PM. This study may help provide an effective way to deal with planning problems with conflicting and sequential goals under uncertainty and make better tradeoff strategies in EWEN optimization in EPS.

Energy-water nexus planning of regional electric power system within an inexact optimization model in Tangshan City, China

Under the increasingly serious energy and water resources crisis, the correlation between two resources has become more and more close. Promoting the sustainable development of energy and water resources has been an inevitable choice for regional energy system planning. In this study, a hybrid interval two-stage fuzzy credibility constrained programming (ITFCP) model is developed for supporting energy-water nexus system management. ITFCP can explicitly address system uncertainties presented as probability distributions, fuzzy sets, and intervals, as well as dual dynamics through the concept of solutions with interval membership function. The method could provide effective linkages between predefined policies and economic penalties as well as system economy and risk. Then, ITFCP is applied to electric power system management under considering energy-water nexus in Tangshan City, which is a typical resource-based, high pollution, and water-deficient region in China. Multiple scenarios of different credibility levels about water availability are designed. Results disclosed that coal-fired power and imported electricity would play primary role in meeting tremendous electricity demand under the current situation. Results also revealed that the total water consumption control would be an effective way to accelerate electric power structural adjustment, protect energy and water resources as well as improve atmospheric environment. These findings are valuable for decision makers to gain in-depth analysis for more reasonable and applicable development patterns within a complex energy-water system.

A Stochastic Optimization Model for Carbon-Emission Reduction Investment and Sustainable Energy Planning under Cost-Risk Control

Restricted by conventional energy resources and environmental space, the sustainable development of urban power sector faces enormous challenges. Renewable energy generation and carbon capture and storage (CCS) are attractive technologies for reducing conventional energy resource consumption and improving CO2 emission mitigation. Considering the limitation of expensive investment cost on their wide application, a stochastic optimization model for the optimal design and operation strategy of regional electric power system is proposed to achieve conventional resource-consumption reduction and CO2 emission mitigation under cost-risk control. The hybrid method integrates interval two-stage stochastic programming with downside risk theory. It can not only effectively deal with the complex uncertainties expressed as discrete intervals and probability distribution, but also help decision-makers make cost-risk tradeoff under predetermined budget. The proposed model is applied in the electric power system planning of Zhejiang Province, an economically developed area with limited fossil energy resources. The influences of different resource and environmental policies on the investment portfolio and power system operation are analyzed and discussed under various scenarios. The results indicated that different policies would lead to different generation technology portfolios. The aggressive CO2 emission reduction policy could stimulate the development of CCS technology, and the electric power system would still heavily rely on coal resource, while the tough coal-consumption control policy could directly promote regional renewable energy development and electric power structure adjustment.

Robust regional low-carbon electricity system planning with energy-water nexus under uncertainties and complex policy guidelines

Abstract This paper develops an interval multi-stage robust programming model for regional low-carbon electric power system planning with energy-water nexus. The model integrates interval programming and multi-stage stochastic programming to handle uncertainties with different probability distribution information. In addition, under the framework of robust programming, the optimal solutions could provide tradeoff information between system cost and decision maker’s risk preference. The hybrid programming method is tailored for a case study of Shandong Province, China to validate its applicability and provide useful managerial insights, where energy-water interactions are gain great concerns. The model helps to make robust strategies associated with risk control for capacity expansion, carbon capture and storage (CCS) technology retrofitting, power generation, imported electricity, and water allocation for electricity generation, with the goal of minimizing total cost. Besides, considering the variation of water resource conditions and environmental policies under climate change, the impacts of water resource availability and carbon emission caps on regional electric power system planning are explored and discussed. It is found that stricter carbon emission cap policy would facilitate the investment on CCS devices but have little impact on the installed capacity structure, meanwhile it would increase water consumption per power generation. However, scarcer water resource situation would promote the development of renewable energy generation, and limit the investment on CCS technology.

Integrated Planning for Regional Electric Power System Management with Risk Measure and Carbon Emission Constraints: A Case Study of the Xinjiang Uygur Autonomous Region, China

With the carbon reduction targets being set in the Paris Agreement on Climate Change, China is facing great pressure to meet its emission reduction commitment. The electric power industry as the major source of carbon emissions needs to be a focus. However, the uncertainty of power systems, the risk of reducing emissions and the fuzziness of carbon capture technology popularization rate and carbon reduction targets makes previous planning methods unsatisfactory for current planning. This paper establishes an interval fuzzy programming with a risk measure model which takes carbon capture technology and carbon reduction targets into account, to ensure that the complex electric management system achieves the best developmental state. It was concluded that in order to reduce carbon emissions, wind power and hydropower would be the best choices, and coal-fired power would be the suboptimal choice, and solar power would play a complementary role. Besides, decision makers should put much more effort into promoting and improving carbon capture technology instead of simply setting emission reduction targets. The non-synchronism of the downward trend in carbon emissions per unit of electricity generation and electric power industry total carbon emissions need to be taken seriously.

An inexact multistage fuzzy-stochastic programming for regional electric power system management constrained by environmental quality

Electric power system involves different fields and disciplines which addressed the economic system, energy system, and environment system. Inner uncertainty of this compound system would be an inevitable problem. Therefore, an inexact multistage fuzzy-stochastic programming (IMFSP) was developed for regional electric power system management constrained by environmental quality. A model which concluded interval-parameter programming, multistage stochastic programming, and fuzzy probability distribution was built to reflect the uncertain information and dynamic variation in the case study, and the scenarios under different credibility degrees were considered. For all scenarios under consideration, corrective actions were allowed to be taken dynamically in accordance with the pre-regulated policies and the uncertainties in reality. The results suggest that the methodology is applicable to handle the uncertainty of regional electric power management systems and help the decision makers to establish an effective development plan.

An inexact optimization model for regional electric system steady operation management considering integrated renewable resources

In this study, an inexact two-stage stochastic fuzzy programming (ITSFP) is developed for regional power generation planning with considering the intermittency and fuzziness of renewable energy power output. ITSFP incorporates interval-parameter programming (IPP), two-stage stochastic programming (TSP), and fuzzy credibility constrained programming (FCCP) within a general optimization framework which can tackle uncertainties expressed as intervals, probability distributions, and fuzzy sets. The developed method is applied to a regional electric power system over a one-day optimization horizon coupled with air pollution control. The power generation schemes, imported electricity, and system cost under various environmental goals and risk preferences are analyzed. The obtained results indicate that the model can provide a linkage between predefined electric power generation schedule and the relevant economic implications, as well as more reasonable decision alternatives for decision makers by loosening system constraints at specified confidence level. Besides, the fuzziness of forecast error corresponding to the variability of renewable energy resources could be effectively reflected. Moreover, the results are useful for addressing the trade-off between system economy and system risk.

Planning a sustainable urban electric power system with considering effects of new energy resources and clean production levels under uncertainty: A case study of Tianjin, China

This study develops a risk-aversion optimization model for an urban electric power system (RAOM-UEPS), taking into account stochastic uncertainties. The RAOM-UEPS can manage stochastic uncertainties and capture associated risks from the stochastic information. This enables managers to analyze the trade-off between system cost and system risk in detail. Then, as a case study, the RAOM-UEPS is applied to the planning of an urban electric power system in Tianjin. Here, three scenarios are considered, each with different proportions of new energy resources and clean production levels (i.e., energy conversion efficiencies). This study aims to develop an urban electric power system (UEPS) optimization model that support the city's transformation from a coal-fired dominated to a low-carbon electric power mix, as well as to promote the sustainable development of society as a whole. The proposed model can facilitate a sophisticated system analysis of energy supply, electric power conversion, capacity expansion, and environment management over multiple periods. The results suggest that coal is dominant in Tianjin's electric power system, which was the primary air-pollutants and CO2 contributor in electric power system. Improving clean production levels and the proportion of new energy resources could effectively save energy resources and mitigate air pollutants and CO2 emissions. These findings can provide a scientific basis for the sustainable development of regional electric power systems, as well as for transformation from coal-dominated to low-carbon electric power cities.

Electric power system planning with renewable energy accommodation for supporting the sustainable development of Tangshan City, China

In this study, considering break the original energy structure dominated by coal and make the most use of renewable energy in the future, an inexact stochastic robust mixed-integer programming (ITSRMP) method was developed for supporting regional electric power system management in Tangshan City, China. The developed method incorporated interval-parameter programming (IPP), stochastic robust optimization (SRO), two-stage stochastic programming (TSP), and mixed integer programming (MIP) within a general optimization framework to reflect uncertainties expressed as interval values and probability distributions in the regional electric power system. Three scenarios corresponding to different subsidy price levels and three cases associated with different pollutants emission reduction levels were designed. The electricity generation schemes, facility-expansion, pollutant emission, and system cost considering the subsidy policy and air pollution mitigation control had been obtained. The results indicated that subsidy policy would exert an important influence on the development of Tangshan's electric power system, which can reduce the cost advantage of conventional power generation and enhance the development enthusiasm of renewable power generation to power enterprises. In detail, the electricity generation amount of renewable energy would increase with the improvement of subsidy price level. Moreover, decision makers could identify the possible policy implementations and enforcements under considering the trade-off among system economy, security and environmental objectives. The modeling results were valuable for promoting new energy accommodation and supporting the sustainable development of social economy.

An Inexact Credibility Chance-Constrained Integer Programming for Greenhouse Gas Mitigation Management in Regional Electric Power System under Uncertainty

Electric power system (EPS) management considering greenhouse gas (GHG) mitigation is a challenging task, since many system parameters such as electric demand, resource availability, system cost as well as their interrelationships may appear uncertain. To reflect these uncertainties, in this study, an interval-parameter credibility constrained programming (ICCP) method was developed for electric power system planning in light of GHG mitigation. The method was advantageous in tackling uncertainties expressed as not only fuzzy possibilistic distributions associated with the right-hand-side components of model constraints but also discrete intervals in the objective function. In addition, ICCP allowed satisfaction of system constraints at specified confidence level, leading to model solutions with low system cost under acceptable risk magnitudes. The obtained results indicated that stable intervals for the objective function and decision variables could be generated, which were useful for helping decision makers identify the desired electric power generation patterns, capacity expansion schemes and GHG-emission reduction under complex uncertainties, and gain in-depth insights into the trade-offs between system economy and reliability.