Ken Nagasaka

Multiobjective Intelligent Energy Management for a Microgrid

In this paper, a generalized formulation for intelligent energy management of a microgrid is proposed using artificial intelligence techniques jointly with linear-programming-based multiobjective optimization. The proposed multiobjective intelligent energy management aims to minimize the operation cost and the environmental impact of a microgrid, taking into account its preoperational variables as future availability of renewable energies and load demand (LD). An artificial neural network ensemble is developed to predict 24-h-ahead photovoltaic generation and 1-h-ahead wind power generation and LD. The proposed machine learning is characterized by enhanced learning model and generalization capability. The efficiency of the microgrid operation strongly depends on the battery scheduling process, which cannot be achieved through conventional optimization formulation. In this paper, a fuzzy logic expert system is used for battery scheduling. The proposed approach can handle uncertainties regarding to the fuzzy environment of the overall microgrid operation and the uncertainty related to the forecasted parameters. The results show considerable minimization on operation cost and emission level compared to literature microgrid energy management approaches based on opportunity charging and Heuristic Flowchart (HF) battery management.

A Novel Microcontroller for Grid-Connected Photovoltaic Systems

The purpose of this paper is to develop a novel microcontroller for grid-connected photovoltaic (PV) systems. As a prototype model, a 100-W-class module-integrated converter composed of the proposed controller and a flyback inverter has been built and tested. The prototype model is designed to satisfy the Japanese grid-connection guideline. Basic functions as those of a grid-connected PV inverter, such as the maximum-power-point tracking and the anti-islanding protection, have been confirmed in the experiments using a distribution network simulator located in a laboratory. This paper presents the description of the controller and the experimental results. A microcontroller has been developed with a 50-MHz-class microcomputer and simple interfaces. By revising the program, the proposed controller can be applied to various types of PV systems or grid-connected equipment

Three Control Strategies to Improve the Microgrid Transient Dynamic Response During Isolated Mode: A Comparative Study

The necessity to solve global warming problems by reducing CO2 emission in the electricity generation field had led to increasing interest in microgrids (MGs), particularly those containing the renewable sources such as solar and wind generation. Wind speed fluctuations cause high variations in the output power of a wind turbine which cause fluctuations in frequency and voltage of the MG during islanding mode and originate stability problems. In this paper, three techniques are proposed for solving and reducing the consequences of this problem. In the first technique, we develop a new fuzzy logic pitch angle controller. In the second technique, we design an energy-storage ultracapacitor which directly smoothes the output power of the wind turbine and enhances the performance of the MG during the islanding mode. In the third technique, storage batteries are used to support the MG in the islanding mode.

Artificial neural networks applied to long-term electricity demand forecasting

The electric power demand in Japan has steadily increased and the load factor of total power system has decreased. It is therefore very important to the utilities to have advance knowledge of their electrical load. One of the important points for forecasting the long-term load in Japan is to take into account the past and present economic situations and power demand. These points were considered in this study. The proposed artificial neural network (ANN) that is radial basis function network (RBFN) has also showed that the changes in loads are a reflection of economy. Here, prediction of peak loads in Japan up to year 2015 is discussed using the RBFN and the maximum demands for 2001 through 2015 are predicted to be elevated from 179.42 GW to 209.18 GW. The annual average rate of load growth seen per ten years until 2015 is about 1.39%.

Neural Network Ensemble-Based Solar Power Generation Short-Term Forecasting

This paper presents the applicability of artificial neural networks for 24 hour ahead solar power generation forecasting of a 20 kW photovoltaic system, the developed forecasting is suitable for a reliable Microgrid energy management. In total four neural networks were proposed, namely: multi-layred perceptron, radial basis function, recurrent and a neural network ensemble consisting in ensemble of bagged networks. Forecasting reliability of the proposed neural networks was carried out in terms forecasting error performance basing on statistical and graphical methods. The experimental results showed that all the proposed networks achieved an acceptable forecasting accuracy. In term of comparison the neural network ensemble gives the highest precision forecasting comparing to the conventional networks. In fact, each network of the ensemble over-fits to some extent and leads to a diversity which enhances the noise tolerance and the forecasting generalization performance comparing to the conventional networks. Keywords—Neural network ensemble, Solar power generation, 24 hour forecasting, Comparative study.

Long-term peak demand prediction of 9 Japanese power utilities using radial basis function networks

Prediction of peak loads in Japan up to year 2010 is discussed using the radial basis function networks (RBFNs). In this study, total system load forecast reflecting the current and future trends is carried out for 9 power companies in Japan. Predictions were done for target years 2001 to 2010 respectively. Unlike short-term load forecasting, long-term load forecasting is mainly affected by economy factors rather than weather conditions. This study focuses on economical data that seem to have influence on long-term electric load demand. The data used are: actual yearly, incremental growth rate from previous year, and blend (actual and incremental growth rate from previous years). As the results, the maximum demands for 2001 through 2010 are predicted and is shown to be elevated from 171.42 GW to 198.60 GW for entire 9 Japanese Power Utilities. The annual average rate of load growth seen per ten years until 2010 is about 1.3%.

Microgrid efficiency enhancement based on neuro-fuzzy MPPT control for Photovoltaic generator

In terms of optimal Microgrid (MG) control, the output power of a non-dispatchable Distributed Generation (DG) as a Photovoltaic (PV) system need to be controlled based on the optimal operating condition of its primary energy source by the mean of a Maximum Power Point Tracking (MPPT) to extract the potential maximum power which is nonlinearly depending on the weather conditions. In this work we presented a new methodology for this purpose using an approach based on a neuro-fuzzy generalized method to estimate the reference voltage (V*pv) that guaranties an optimal power transfer between the DG and the microgrid. The neuro-fuzzy network is composed of a fuzzy rule-based classifier and three Radial Basis Functions Neural Networks (RBFNN). Inputs of the network (irradiance and temperature) are classified before they are fed into the appropriated network for either training or estimation process. Simulation results under several rapid irradiance variations proved that the proposed MPPT control for the PV generator achieved high energy conversion efficiency comparing to a Normal Operating Power (NOP) (when the PV generator is directly coupled to the inverter, without MPPT control).

Micro-grid Dynamic Response Enhancement Using New Proportional Integral Wind Turbine Pitch Controller and Neuro-fuzzy Photovoltaic Maximum Power Point Tracking Controller

Abstract Power deregulation and shortage of transmission capacities have led to increased interest in distributed generators, especially renewable sources. In this study, a complete model is developed that can simulate in detail the transient dynamic performance of the micro-grid during and subsequent to the islanding process. Wind speed fluctuations cause high fluctuations in output power of a wind turbine, resulting in fluctuations in frequency and voltages of the micro-grid during islanding mode. In this study, new proportional integral pitch controller is proposed to smooth the output power of a wind turbine to reduce frequency and voltage fluctuations. The proposed proportional integral controller is compared with the conventional proportional integral controller that is used for wind turbine power control. The obtained results proved that the proposed controller is effective for the improvement of micro-grid performances. In addition, a neuro-fuzzy controller is also proposed for obtaining maximum power point tracking of the photovoltaic panels installed in the micro-grid. All models and controllers are developed on a Matlab® Simulink® environment.

Toward Designing Value Supportive Infrastructure for Electricity Trading

The recent deregulation process of the electric power industry in Japan motivates formulating a new kind of models for designing e-commerce-oriented infrastructure with a real-time electronic trading system at its core in the Internet environment. This paper aims at presenting an elemental market-based model of the wholesale electricity trading. The model is to expected to provide building blocks for studying the interactions between elements of market design and various market forces. It is, also, intended to facilitate the assessment of alternative policies for forming the regulatory rules of trading. We view the electric power network as a platform on which trading games by generating firms, energy users and the system operator take place. Economic values of transmission grid-resources will be priced from the framework of how these resources are to be shared by the competing market participants seeking benefits of their own. The phenomena of the market splits, emergence of different prices within a linked system of sub-markets, are modeled and derived as part of the equilibrium. The model will also help the infrastructure planner identify various resource bottlenecks in the present system and formulate strategy for future development.

Planning of micro-grid power supply based on the weak wind and hydro power generation

This paper presents the installation and verification processes of micro hydro power plant (MHPP) and micro wind power turbine (MWPT) for Kuromori Mountainous Region of Yamanashi Profecture based on the planning project of COE Program. This research focuses on the technologies to develop and utilize the natural energy in a region where the average wind speed (AWS) and water discharge are relatively weak just like the most other regions in this prefecture. micro-grid (MG) consisting of MHPP and MWPT sources are to be installed in existing power system (EPS). Contribution of MHPP and MWPT are determined by investigated wind and water characteristics on complex shaped mountainous land. Stability and reliability of the new hybrid power system are proved by using the compound controllers. Benefit from MG of MHPP and MWPT is proved by comparing the environmental and financial efficiency before and after installing the MG. This research, with the important practical significance, indicated that regions with relatively weak natural energy may be developed by applying the MG with possible compensation between micro-sources