2021 International Conference on Power, Energy and Innovations (ICPEI), 2021
This paper proposes the optimization of electric power from a microgrid system which consists of ... more This paper proposes the optimization of electric power from a microgrid system which consists of PV, ESS and grid. The Home Energy Management System (HEMS) is a home energy management system that can collect data on various electrical usage in the home. Both the production system and the use of electricity. The production is connected to the grid utility and ESS for efficiency optimization with the load. This can reduce the cost of the energy storage system and reduce the electricity cost.
2021 9th International Electrical Engineering Congress (iEECON), 2021
Rising demand for energy consumption indicates that adequate energy supplies are essential for ec... more Rising demand for energy consumption indicates that adequate energy supplies are essential for economic growth and social development. Nowadays, as fossil fuel is running out and discharging a huge amount of carbon emission, energy transformation to renewables is a must. However, developing and operating clean power plants are a momentous challenge as it is difficult to manage sustainable and long-lasting energy resources. A forecasting model is, therefore, a promising tool to predict the generation, consumption, and reservation of energy.In this paper, a long-term forecasting model for hydropower production using the autoregressive integrated moving average (ARIMA) time series method is proposed. The collected data was obtained from the Son La hydropower plant in Vietnam. The electricity generation in this plant demonstrates an upward trend in the future. Although the power capacity of the hydropower plant is significantly affected by environmental variability, having a forecasting model and a long-term plan will greatly benefit renewable energy production to keep up with economic growth. In addition, the simulation results can be used as a reference for further studies and strategic energy planning.
2014 International Conference and Utility Exhibition on Green Energy For Sustainable Development, Mar 19, 2014
In this paper, robust optimization based AC optimal power flow (ROPF) considering wind and solar ... more In this paper, robust optimization based AC optimal power flow (ROPF) considering wind and solar power uncertainty is proposed. ROPF is used to determine optimal power dispatch and locational marginal prices in a day-ahead market while limiting the risk of dispatch cost variation. ROPF is tested on PJM 5-bus system integrating wind and solar PV generation. Simulation results indicate that ROPF results in a lower expected dispatch cost at the same risk preference level than a stochastic nonlinear programming (SNP) approach. Accordingly, it is potentially useful for day-ahead market operator in policy and decision making.
International Journal of Energy Optimization and Engineering, 2013
This paper proposes a new improved particle swarm optimization (NIPSO) for solving nonconvex econ... more This paper proposes a new improved particle swarm optimization (NIPSO) for solving nonconvex economic dispatch (ED) problem in power systems including multiple fuel options (MFO) and valve-point loading effects (VPLE). The proposed NIPSO method is based on the self-organizing hierarchical (SOH) particle swarm optimizer with time-varying acceleration coefficients (TVAC). The self-organizing hierarchical can handle the premature convergence of the problem by re-initialization of velocity whenever particles are stagnated in the search space. During the optimization process, the performance of TVAC is applied for properly controlling both local and global explorations with cognitive component and social component of the swarm to obtain the optimum solution accurately and efficiently. The proposed NIPSO algorithm is tested in different types of non-smooth cost functions for solving ED problems and the obtained results are compared to those from many other methods in the literature. The r...
This paper presents an analytical approach to determine the optimal location and size of distribu... more This paper presents an analytical approach to determine the optimal location and size of distributed generation (DG) in the electrical distribution system of Naresuan University (NU). Based on available data of the system, the single line diagram is first drawn and line impedances among buses are estimated. The latter values are calculated based on the distance between bus locations and the electrical conductor characteristics. According to the power transformer rates and the maximum total load of the NU system (13.60 MW), the load consumptions of all main loads connected to the NU buses can be assumed. The optimal size and location of DG have been determined to minimize the transmission losses in the system. Placement of a photovoltaic source known as type-I DG has been considered for injecting the real power into the system. The analytical approach is based on the exact loss formula. The effects of optimal size and location of DG are considered and examined in detail in order to find minimum losses at various bus locations. The results show that the proposed analytical approach can reduce the transmission loss to 17.65 kW at the optimum size of DG of 7.58 MW at the bus no. 13, which is located near the main load (the NU hospital). This work enhances our understanding of the current system performance and allows us to plan for future improvement of the distribution system.
This article proposes an augmented Lagrange–Hopfield network for the combined heat and power econ... more This article proposes an augmented Lagrange–Hopfield network for the combined heat and power economic dispatch problem. The augmented Lagrange–Hopfield network method is the continuous Hopfield neural network with its energy function based on augmented Lagrangian relaxation. In the proposed augmented Lagrange–Hopfield network, the energy function is augmented by Hopfield terms from the Hopfield neural network and penalty factors from the
This paper proposes a self-organizing hierarchical particle swarm optimization (SPSO) with time-v... more This paper proposes a self-organizing hierarchical particle swarm optimization (SPSO) with time-varying acceleration coefficients (TVAC) for solving economic dispatch (ED) problem with non-smooth functions including multiple fuel options (MFO) and valve-point loading effects (VPLE). The proposed SPSO with TVAC is the new approach optimizer and good performance for solving ED problems. It can handle the premature convergence of the problem by re-initialization of velocity whenever particles are stagnated in the search space. To properly control both local and global explorations of the swarm during the optimization process, the performance of TVAC is included. The proposed method is tested in different ED problems with non-smooth cost functions and the obtained results are compared to those from many other methods in the literature. The results have revealed that the proposed SPSO with TVAC is effective in finding higher quality solutions for non-smooth ED problems than many other methods.
This paper proposes an augmented Lagrange Hopfield network (ALHN) for solving economic dispatch (... more This paper proposes an augmented Lagrange Hopfield network (ALHN) for solving economic dispatch (ED) problem with multiple fuel options. The proposed ALHN method is a continuous Hopfield neural network with its energy function based on augmented Lagrangian function. The advantages of ALHN over the conventional Hopfield neural network are easier use, more general applications, faster convergence, better optimal solution, and larger scale of problem implementation. The method solves the problem by directly searching the most suitable fuel among the available fuels of each unit and finding the optimal solution for the problem based on minimization of the energy function of the continuous Hopfield neural network. The proposed method is tested on systems up to 100 units and the obtained results are compared to those from other methods in the literature. The results have shown that the proposed method is efficient for solving the ED problem with multiple fuel options and favorable for implementation in large scale problems.
2021 International Conference on Power, Energy and Innovations (ICPEI), 2021
This paper proposes the optimization of electric power from a microgrid system which consists of ... more This paper proposes the optimization of electric power from a microgrid system which consists of PV, ESS and grid. The Home Energy Management System (HEMS) is a home energy management system that can collect data on various electrical usage in the home. Both the production system and the use of electricity. The production is connected to the grid utility and ESS for efficiency optimization with the load. This can reduce the cost of the energy storage system and reduce the electricity cost.
2021 9th International Electrical Engineering Congress (iEECON), 2021
Rising demand for energy consumption indicates that adequate energy supplies are essential for ec... more Rising demand for energy consumption indicates that adequate energy supplies are essential for economic growth and social development. Nowadays, as fossil fuel is running out and discharging a huge amount of carbon emission, energy transformation to renewables is a must. However, developing and operating clean power plants are a momentous challenge as it is difficult to manage sustainable and long-lasting energy resources. A forecasting model is, therefore, a promising tool to predict the generation, consumption, and reservation of energy.In this paper, a long-term forecasting model for hydropower production using the autoregressive integrated moving average (ARIMA) time series method is proposed. The collected data was obtained from the Son La hydropower plant in Vietnam. The electricity generation in this plant demonstrates an upward trend in the future. Although the power capacity of the hydropower plant is significantly affected by environmental variability, having a forecasting model and a long-term plan will greatly benefit renewable energy production to keep up with economic growth. In addition, the simulation results can be used as a reference for further studies and strategic energy planning.
2014 International Conference and Utility Exhibition on Green Energy For Sustainable Development, Mar 19, 2014
In this paper, robust optimization based AC optimal power flow (ROPF) considering wind and solar ... more In this paper, robust optimization based AC optimal power flow (ROPF) considering wind and solar power uncertainty is proposed. ROPF is used to determine optimal power dispatch and locational marginal prices in a day-ahead market while limiting the risk of dispatch cost variation. ROPF is tested on PJM 5-bus system integrating wind and solar PV generation. Simulation results indicate that ROPF results in a lower expected dispatch cost at the same risk preference level than a stochastic nonlinear programming (SNP) approach. Accordingly, it is potentially useful for day-ahead market operator in policy and decision making.
International Journal of Energy Optimization and Engineering, 2013
This paper proposes a new improved particle swarm optimization (NIPSO) for solving nonconvex econ... more This paper proposes a new improved particle swarm optimization (NIPSO) for solving nonconvex economic dispatch (ED) problem in power systems including multiple fuel options (MFO) and valve-point loading effects (VPLE). The proposed NIPSO method is based on the self-organizing hierarchical (SOH) particle swarm optimizer with time-varying acceleration coefficients (TVAC). The self-organizing hierarchical can handle the premature convergence of the problem by re-initialization of velocity whenever particles are stagnated in the search space. During the optimization process, the performance of TVAC is applied for properly controlling both local and global explorations with cognitive component and social component of the swarm to obtain the optimum solution accurately and efficiently. The proposed NIPSO algorithm is tested in different types of non-smooth cost functions for solving ED problems and the obtained results are compared to those from many other methods in the literature. The r...
This paper presents an analytical approach to determine the optimal location and size of distribu... more This paper presents an analytical approach to determine the optimal location and size of distributed generation (DG) in the electrical distribution system of Naresuan University (NU). Based on available data of the system, the single line diagram is first drawn and line impedances among buses are estimated. The latter values are calculated based on the distance between bus locations and the electrical conductor characteristics. According to the power transformer rates and the maximum total load of the NU system (13.60 MW), the load consumptions of all main loads connected to the NU buses can be assumed. The optimal size and location of DG have been determined to minimize the transmission losses in the system. Placement of a photovoltaic source known as type-I DG has been considered for injecting the real power into the system. The analytical approach is based on the exact loss formula. The effects of optimal size and location of DG are considered and examined in detail in order to find minimum losses at various bus locations. The results show that the proposed analytical approach can reduce the transmission loss to 17.65 kW at the optimum size of DG of 7.58 MW at the bus no. 13, which is located near the main load (the NU hospital). This work enhances our understanding of the current system performance and allows us to plan for future improvement of the distribution system.
This article proposes an augmented Lagrange–Hopfield network for the combined heat and power econ... more This article proposes an augmented Lagrange–Hopfield network for the combined heat and power economic dispatch problem. The augmented Lagrange–Hopfield network method is the continuous Hopfield neural network with its energy function based on augmented Lagrangian relaxation. In the proposed augmented Lagrange–Hopfield network, the energy function is augmented by Hopfield terms from the Hopfield neural network and penalty factors from the
This paper proposes a self-organizing hierarchical particle swarm optimization (SPSO) with time-v... more This paper proposes a self-organizing hierarchical particle swarm optimization (SPSO) with time-varying acceleration coefficients (TVAC) for solving economic dispatch (ED) problem with non-smooth functions including multiple fuel options (MFO) and valve-point loading effects (VPLE). The proposed SPSO with TVAC is the new approach optimizer and good performance for solving ED problems. It can handle the premature convergence of the problem by re-initialization of velocity whenever particles are stagnated in the search space. To properly control both local and global explorations of the swarm during the optimization process, the performance of TVAC is included. The proposed method is tested in different ED problems with non-smooth cost functions and the obtained results are compared to those from many other methods in the literature. The results have revealed that the proposed SPSO with TVAC is effective in finding higher quality solutions for non-smooth ED problems than many other methods.
This paper proposes an augmented Lagrange Hopfield network (ALHN) for solving economic dispatch (... more This paper proposes an augmented Lagrange Hopfield network (ALHN) for solving economic dispatch (ED) problem with multiple fuel options. The proposed ALHN method is a continuous Hopfield neural network with its energy function based on augmented Lagrangian function. The advantages of ALHN over the conventional Hopfield neural network are easier use, more general applications, faster convergence, better optimal solution, and larger scale of problem implementation. The method solves the problem by directly searching the most suitable fuel among the available fuels of each unit and finding the optimal solution for the problem based on minimization of the energy function of the continuous Hopfield neural network. The proposed method is tested on systems up to 100 units and the obtained results are compared to those from other methods in the literature. The results have shown that the proposed method is efficient for solving the ED problem with multiple fuel options and favorable for implementation in large scale problems.
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Papers by Jirawadee Polprasert