DESIGNING OF MARKET MODEL, EFFECTIVE PRICE FORECASTING TOOL AND BIDDING STRATEGY FOR INDIAN ELECTRICITY MARKET

Abstract

The Development scenario for renewable energy across the globe is changing rapidly in terms of capacity addition and grid interconnection. Penetration of renewable energy resources into grid is necessary to meet the elevated demand of electricity. In view of this penetration of solar and wind power growing enormously across the globe. Solar energy is widely escalating in terms of generation and capacity addition due its better predictability over wind energy. Electricity pricing is one of the important aspects for power system planning and it felicitates information for the electricity bidder for exact electricity generation and resource allocation. The important task is to forecast the electricity price accurately in grid interactive environment. This task is tedious in renewable integrated market due to intermittency issue. As renewable energy penetration into the grid is enhancing swiftly. An appropriate market model addressing the issues of related to renewable energy specially wind and solar is necessary. A novel solar energy-based market model is proposed for state level market along with the operating mechanism. The different component associated with grid and their functionality in the operation of grid is discussed. Challenges and possible solutions are addressed to implement the market model. Energy trading plays a crucial role in the economic growth of country. Renewable energy trading opens a new avenue for the economic growth. India is blessed with a rich solar energy resources, the solar power producers tapped the potential of solar up to appreciable extent, but due to lack of trading models and specific regulatory mechanism in context of renewable energy generation is main hurdle in competition among generators. Various market model developed for solar energy trading at state level electricity along with their trading mechanism is presented. Also features of the models are also addressed.

xiii A Rigorous literature review on price forecasting is conducted with focus on impact of solar and wind energy on electricity price. The data of Australia electricity market is collected for price forecasting. The correlation among the inputs for price is calculated using correlation coefficient formula and selected the highly corelated input with price. Artificial Neural Network (ANN) is implemented to forecast the price by using historical data. The price is predicted for January to June month and weekly forecast of price for the same month is executed. The minimum MAPE is 1.94 for April month and 1.03 for third week of January. The research work is continued to investigate the impact of solar and wind energy on electricity price. The Long short-term memory (LSTM) is designed to forecast the electricity price considering the solar power penetration. The raw data of Austria market consists of actual day ahead load, forecasted day ahead load, actual day ahead price and actual solar generation is used. The reliability of forecasting model is analyzed by computation of confidence interval on MAPE. The research work is extended to investigate the impact of wind energy on electricity price. The Austria electricity market data is used for investigating the potential impact of wind energy on rice. The statistical analysis of the data is conducted for finding the suitability of the model. Decision tree model is designed and implemented and significant reduction in the forecasting accuracy of 5.802 is achieved for the data set using wind energy as input parameter. The future of solar energy in India is positive. The growth of solar energy in terms of capacity addition and grid interconnection programme is expanding day by day. To promote the solar energy trading in open market a suitable bidding mechanism must be designed for solar power producers. It becomes pertinent to design the bidding strategy for solar power producers to maximize their profit considering the uncertainty in the energy output. Hybrid Particle Swarm Optimization – Gravitational Search Algorithm (HPSO - GSA) is proposed for designing the optimal bidding strategy for solar PV power producer for designed solar energy

xiv based Indian electricity market. The objective function is designed considering the constraint of uncertainty and energy imbalance in price. The proposed algorithm shows highest profit when compared with Real Coded Genetic Algorithm (RCGA), Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA). In the light of continual renewable energy growth and grid interconnection, a novel solar energy-based electricity market model addressing the issues of solar energy is proposed to make the system effective and reliable. This novel market may fill the promise of providing electricity at competitive cost for all in India. The various market models are proposed for trading the solar energy in competitive market for maximum utilization of untapped potential of solar energy. The various trading models may be implemented based on the application and suitability. The electricity price forecasting is an important aspects of power system planning and for renewable energy interactive grid price forecasting is crucial task due its intermittent nature. ANN model is proposed for price forecasting and significant improvement in MAPE is reported for Australia electricity market data. Further the investigation has been done on the impact of solar energy generation on electricity price using machine learning techniques (DT, RF, LASSO, XGBOOST and LSTM). The LSTM model accuracy is good in price forecasting with consideration of solar energy as input parameter. The investigation is extended for impact of wind energy on electricity price and Decision tree model accuracy is superior as compared to RF, LASSO, LR, SVR and DNN model. The bidding strategy for the designed solar based electricity model is proposed using HPSO-GSA method and profit calculation has been done for solar PV producers on real time data. The maximized profit has been obtained through HSPO-GSA method for two different sets of datasets.