Improved particle swarm optimization algorithm for multi-reservoir system operation

AbstractIn this paper, a hybrid improved particle swarm optimization (IPSO) algorithm is proposed for the optimization of hydroelectric power scheduling in multi-reservoir systems. The conventional particle swarm optimization (PSO) algorithm is improved in two ways: (1) The linearly decreasing inertia weight coefficient (LDIWC) is replaced by a self-adaptive exponential inertia weight coefficient (SEIWC), which could make the PSO algorithm more balanceable and more effective in both global and local searches. (2) The crossover and mutation idea inspired by the genetic algorithm (GA) is imported into the particle updating method to enhance the diversity of populations. The potential ability of IPSO in nonlinear numerical function optimization was first tested with three classical benchmark functions. Then, a long-term multi-reservoir system operation model based on IPSO was designed and a case study was carried out in the Minjiang Basin in China, where there is a power system consisting of 26 hydroelectric power plants. The scheduling results of the IPSO algorithm were found to outperform PSO and to be comparable with the results of the dynamic programming successive approximation (DPSA) algorithm

Modelling of human expert decision making in reservoir operation

Reservoir is one of the structural approaches for flood mitigation and water supply.During heavy raining season, reservoir operator has to determine fast and accurate decision in order to maintain both reservoir and downstream river water level. In contrast to less rainfall season, the reservoir needs to impound water for the water supply purposes.This study is aimed to model human expert decision making specifically on reservoir water release decision.Reservoir water release decision is crucial as reservoir serve multi purposes.The reservoir water release decision pattern that comprises of upstream rainfall and current reservoir water level has been form using sliding window technique.The computational intelligence method called artificial neural network was used to model the decision making

Long term hydrothermal scheduling of the brazilian integrated system based on model predictive control

Orientador: Secundino Soares FilhoTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de ComputaçãoResumo: O planejamento da operação energética do Sistema Interligado Nacional (SIN) é uma tarefa complexa realizada por meio de uma cadeia de modelos de médio, curto e curtíssimo prazo acoplados entre si, cada um com considerações pertinentes à etapa que aborda. A proposta deste trabalho é apresentar uma alternativa para o planejamento da operação energética de médio prazo. Foi desenvolvida uma metodologia baseada em modelo de controle preditivo, abordando os aspectos estocásticos do problema de forma implícita pela utilização de valores esperados das vazões, e fazendo uso de um modelo determinístico de otimização a usinas individualizadas, que possibilita uma representação mais precisa do sistema hidrotérmico. A análise de desempenho é feita através de simulações da operação, considerando os parques hidrelétrico e termelétrico que compõem o SIN, com restrições operativas reais, em configuração dinâmica, com plano de expansão e a possibilidade de intercâmbio e importação de mercados vizinhos. Os resultados são comparados aos fornecidos pela metodologia em vigor no setor elétrico brasileiro, notadamente o modelo NEWAVE, que determina as decisões de geração por subsistema, e o modelo Suishi-O, que as desagrega por usinas individualizadasAbstract: The long term hydrothermal scheduling of the Brazilian Integrated System (SIN) is a complex task solved by a chain of long, medium and short term coupled models, each one with considerations pertinent to the stage of operation that it deals with. The proposal of this work is to present an alternative for the long term hydrothermal scheduling. A methodology based on model predictive control was developed, implicitly handling stochastic aspects of the problem by the use of inflows expected values, and making use of a deterministic optimization model to obtain the optimal dispatch for individualized plants, what makes possible a more accurate representation of the hydrothermal system. The performance analysis is made through simulations of the operation, taking into consideration all the hydro and thermal plants that compose the SIN, with real operative constraints, in dynamic configuration, with its expansion plan and the possibility of interchange and importation from neighboring markets. The results are compared with those provided by the approach actually in use by the Brazilian electric sector, specifically the NEWAVE model, which defines the generation decisions for the subsystems, and the Suishi-O model, that disaggregates them for the individualized plantsDoutoradoEnergia EletricaDoutor em Engenharia Elétric

Hydro–connected floating PV renewable energy system and onshore wind potential in Zambia

The adoption of a diversification strategy of the energy mix to include low-water consumption technologies, such as floating photovoltaics (FPV) and onshore wind turbines, would improve the resilience of the Zambian hydro-dependent power system, thereby addressing the consequences of climate change and variability. Four major droughts that were experienced in the past fifteen years in the country exacerbated the problems in load management strategies in the recent past. Against this background, a site appraisal methodology was devised for the potential of linking future and existing hydropower sites with wind and FPV. This appraisal was then applied in Zambia to all the thirteen existing hydropower sites, of which three were screened off, and the remaining ten were scored and ranked according to attribute suitability. A design-scoping methodology was then created that aimed to assess the technical parameters of the national electricity grid, hourly generation profiles of existing scenarios, and the potential of variable renewable energy generation. The results at the case study site revealed that the wind and FPV integration reduced the network's real power losses by 5% and improved the magnitude profile of the voltage at nearby network buses. The onshore wind, along with FPV, also added 341 GWh/year to the national energy generation capacity to meet the 4.93 TWh annual energy demand, in the presence of 4.59 TWh of hydro with a virtual battery storage potential of approximately 7.4% of annual hydropower generation. This was achieved at a competitive levelized cost of electricity of GBP 0.055/kWh. Moreover, floating PV is not being presented as a competitor to ground-mounted systems, but rather as a complementary technology in specific applications (i.e., retrofitting on hydro reservoirs). This study should be extended to all viable water bodies, and grid technical studies should be conducted to provide guidelines for large-scale variable renewable energy source (VRES) integration, ultimately contributing to shaping a resilient and sustainable energy transition

Sustainability Analysis and Environmental Decision-Making Using Simulation, Optimization, and Computational Analytics

Effective environmental decision-making is often challenging and complex, where final solutions frequently possess inherently subjective political and socio-economic components. Consequently, complex sustainability applications in the “real world” frequently employ computational decision-making approaches to construct solutions to problems containing numerous quantitative dimensions and considerable sources of uncertainty. This volume includes a number of such applied computational analytics papers that either create new decision-making methods or provide innovative implementations of existing methods for addressing a wide spectrum of sustainability applications, broadly defined. The disparate contributions all emphasize novel approaches of computational analytics as applied to environmental decision-making and sustainability analysis – be this on the side of optimization, simulation, modelling, computational solution procedures, visual analytics, and/or information technologies

Resource Provision of the Sustainable Development under Global Shocks

This reprint focuses on interdisciplinary research that reveals the problems of resource provision of the economy, both from the perspective of local projects and from the point of view of the creation of global infrastructure that contributes to the achievement of Sustainable Development Goals. Considerable attention is paid to the development of the Arctic territories as one of the most promising sources of mineral and fuel resources as of 2021. This reprint also includes selected papers from European Raw Materials Conferences 2020–2021, held despite the global COVID-19 pandemic, and will be published with the financial support of the International competence Centre for mining-engineering education under the auspices of UNESCO: - Russian–UK Raw Materials Dialogue (21–23 October 2020); - Russian–German Raw Materials Conference (30 November–1 December 2020)

XVIII International Coal Preparation Congress

Changes in economic and market conditions of mineral raw materials in recent years have greatly increased demands on the ef fi ciency of mining production. This is certainly true of the coal industry. World coal consumption is growing faster than other types of fuel and in the past year it exceeded 7.6 billion tons. Coal extraction and processing technology are continuously evolving, becoming more economical and environmentally friendly. “ Clean coal ” technology is becoming increasingly popular. Coal chemistry, production of new materials and pharmacology are now added to the traditional use areas — power industry and metallurgy. The leading role in the development of new areas of coal use belongs to preparation technology and advanced coal processing. Hi-tech modern technology and the increasing interna- tional demand for its effectiveness and ef fi ciency put completely new goals for the University. Our main task is to develop a new generation of workforce capacity and research in line with global trends in the development of science and technology to address critical industry issues. Today Russia, like the rest of the world faces rapid and profound changes affecting all spheres of life. The de fi ning feature of modern era has been a rapid development of high technology, intellectual capital being its main asset and resource. The dynamics of scienti fi c and technological development requires acti- vation of University research activities. The University must be a generator of ideas to meet the needs of the economy and national development. Due to the high intellectual potential, University expert mission becomes more and more called for and is capable of providing professional assessment and building science-based predictions in various fi elds. Coal industry, as well as the whole fuel and energy sector of the global economy is growing fast. Global multinational energy companies are less likely to be under state in fl uence and will soon become the main mechanism for the rapid spread of technologies based on new knowledge. Mineral resources will have an even greater impact on the stability of the economies of many countries. Current progress in the technology of coal-based gas synthesis is not just a change in the traditional energy markets, but the emergence of new products of direct consumption, obtained from coal, such as synthetic fuels, chemicals and agrochemical products. All this requires a revision of the value of coal in the modern world economy

Long-term Hydropower Scheduling Based On Deterministic Nonlinear Optimization And Annual Inflow Forecasting Models

This paper proposes an operational policy for long-term hydropower scheduling based on deterministic nonlinear optimization and annual inflow forecasting models using an open-loop feedback control framework. The optimization model precisely represents hydropower generation by taking into consideration water head as a nonlinear function of storage, discharge and spillage. The inflow is made available by a forecasting model based on a fuzzy inference system that captures the nonlinear correlation of consecutive inflows on an annual basis, then disaggregating it on a monthly basis. In order to focus on the ability of the approach to handle the stochastic nature of the problem, a case study with a single-reservoir system is considered. The performance of the proposed approach is evaluated by simulation over the historical inflow records and compared to that of the stochastic dynamic programming approach. The results show that the proposed approach leads to a better operational performance of the plant, providing lower spillages and higher average hydropower efficiency and generation. © 2009 IEEE.Stedinger, J.R., Sule, B.F., Loucks, D.P., Stochastic dynamic programming models for reservoir operation optimization (1984) Water Resources Research, 20 (11), pp. 1499-1505Bellman, R.E., (1957) Dynamic Programming, , Princeton University Press, Princeton, NJArvanitidis, N.V., Rosing, J., Composite representation of a multireservoir hydroelectric power system (1970) IEEE Transactions on Power Apparatus and Systems PAS-89, pp. 319-326Cruz Jr., G., Soares, S., Non-uniforme composite representation of hydroelectric systems for long-term hydrothermal scheduling (1996) IEEE Transactions on Power Systems, 11 (2), pp. 701-707Pereira, M.V.F., Pinto, L.M.V.G., Multi-stage stochastic optimization applied to energy planning (1991) Mathematical Programming, 52 (2), pp. 359-375Valdes, J.B., Filippo, J.M.D., Strzepek, K.M., Restrepo, P.J., Aggregation-disaggregation approach to multireservoir operation (1995) ASCE Journal of Water Resource Planning Management, 121 (5), pp. 345-351Turgeon, A., Optimal operation of multireservoir systems with stochastic inflows (1980) Water Resour. Res., 16 (2), pp. 275-283Labadie, J.W., Optimal operation of multireservoir systems: State-of-the-art review (2004) Journal of Water Resources Planning and Management, 130 (2), pp. 93-111Dembo, R.S., Scenario optimization (1991) Annals of Operations Research, 30 (1), pp. 63-80Escudero, L.F., De La Fuente, J.L., Garcia, C., Prieto, F.J., Hydropower generation management under uncertainty via scenario analysis and parallel computation (1996) IEEE Trans. on Power Syst., 11 (2), pp. 683-689Nabona, N., Multicommodity network flow model for long-term hydrogeneration optimization (1993) IEEE Trans. on Power Syst., 8 (2), pp. 395-404Martinez, L., Soares, S., Comparison between closed-loop and partial open-loop feedback control policies in long-term hydrothermal scheduling (2002) IEEE Trans. on Power Syst., 17 (2)Oliveira, G.G., Soares, S., A second-order network flow algorithm for hydro-thermal scheduling (1995) IEEE Trans. on Power Syst., 10 (3), pp. 1641-1652Rosenthal, R.E., A nonlinear network flow algorithm for maximization of benefits in a hydroelectric power system (1981) Operations Research, 29 (4), pp. 763-785Zambelli, M.S., Siqueira, T.G., Cicogna, M.A., Soares, S., Deterministic versus stochastic models for long-term hydro-thermal scheduling (2006) 2006 IEEE Power Engineering Society General Meeting, , Montreal, Canada, JuneSiqueira, T.G., Zambelli, M.S., Cicogna, M.A., Andrade, M., Soares, S., Stochastic dynamic programming for long-term hydrothermal scheduling considering different streamflow models (2006) PMAPS 2006-9th International Conference on Probabilistic Methods Applied to Power Systems, , Stockholm, Sweden, JuneMitra, S., Hayashi, Y., Neuro-fuzzy rule generation: Survey in soft computing framework (2000) IEEE Transactions on Neural Networks, 11 (3), pp. 748-768. , MaySolomatine, D.P., Siek, M.B., Modular learning models in forecasting natural phenomena (2006) Neural Networks, 10 (2), pp. 215-224Luna, I., Soares, S., Ballini, R., A constructive vs. an online approach for time series prediction (2007) IEEE Procs. of the North American Fuzzy Information Processing Society Meeting-NAFIPS'07, pp. 256-261Takagi, T., Sugeno, M., Fuzzy identification of systems and its applications to modeling and control (1985) IEEE Transactions on Systems, Man and Cybernetics, (1), pp. 116-132. , January/FebruaryChiu, S., A cluster estimation method with extension to fuzzy model identification (1994) Proceedings of the Third IEEE Conference on Fuzzy Systems, 2, pp. 1240-1245. , Orlando-Forida, USAJacobs, R., Jordan, M., Nowlan, S., Hinton, G., Adaptive mixture of local experts (1991) Neural Computation, 3 (1), pp. 79-87Lapide, L., Top-down & bottom-up forecasting in S&OP (2006) Journal of Business Forecasting, 25 (2), pp. 14-1