Opportunities and Challenges in Wind Energy

Wind energy increasingly plays a crucial role in replacing existing systems based on fossil fuels and nuclear generators. However, skepticism about the technological feasibility, economy of scale, operational profitability prevails. Our paper aims to fill this gap by analyzing the affordability of wind energy and by exploring the optimization issue incurring during the integration process of the micro electricity grid. Focusing on Locational Marginal Pricing (LMP) and Midwest Independent System Operator (MISO) data between January 2014 and December 2020, we find wind power generation helps decrease the system energy price after control the effect of gas price. Further, our model captures the important seasonal and uncertain characteristics of power generation and consumption over the years. We also identify and discuss the bottleneck issues of the MISO power grids. These findings provide insights into affordability and technologic integration for long-term strategic planning on green energy including pricing, seasonality, dispatch, and reliability issues.

Opportunities and Challenges in Wind Energy

Wind energy increasingly plays a crucial role in replacing existing systems based on fossil fuels and nuclear generators. However, skepticism about the technological feasibility, economy of scale, and operational profitability prevails. The paper aims to fill this gap by analyzing the affordability of wind energy and by exploring the optimization issue incurring during the integration process of the micro electricity grid. Focusing on locational marginal pricing (LMP) and Midwest independent system operator (MISO) data between January 2014 and December 2020, the authors find wind power generation helps decrease the system energy price after control the effect of gas price. Further, the model captures the important seasonal and uncertain characteristics of power generation and consumption over the years. They also identify and discuss the bottleneck issues of the MISO power grids. These findings provide insights into affordability and technologic integration for long-term strategic planning on green energy including pricing, seasonality, dispatch, and reliability issues.

Feed- Forward Neural Network based Day Ahead Nodal Pricing

An electricity locational marginal pricing prediction normally recognized by 24-hour day-ahead nodal price forecast. In this paper first collected all physical and technical data i.e. availability of generation and their cost characteristics, real and reactive demands at various buses, transmission capacity availability at various conditions like peak and off-peak conditions. All these input data are used as input for computation of optimal power flow. The nodal prices are calculated with AC-DC optimal power flow methodology for IEEE 30 bus system. The resulted optimal real electricity bus voltages, nodal prices, reactive and real demands, angles have been given as inputs to Artificial Neural Network (ANN) for predict day ahead nodal prices.

A Comparative Study of Pricing Mechanisms to Reduce Side-Payments in the Electricity Market: A Case Study for South Korea

Under marginal-cost pricing, some generators cannot recover their production costs at the market price due to non-convexities in the electricity market. For this reason, most electricity markets pay side-payments to generators whose costs are not sufficiently recovered, but side-payments present the problem of deteriorating transparency in the market. Recently, convex hull pricing and extended locational marginal pricing have been reviewed or gradually introduced to reduce side-payments. Another method is to include non-convex costs in the market price, which is applied in the Korean electricity market. Although it is not generally considered in the electricity market, the Vickrey auction method is also one of the pricing mechanisms that can reduce side-payments. The main purpose of this study is to analyze the financial impact of these alternative pricing mechanisms on market participants through rigorous simulation. We applied the alternative pricing schemes to the Korean electricity market, and the impacts are analyzed by comparing the cost aspect of an electricity sales company and the profit aspect of generation companies. As a result of the simulation study, each pricing mechanism not only differed in the degree to which side-payments are reduced but also has different effects on the type of generators.

Locational Marginal Pricing and Daily Operation Scheduling of a Hydro-Thermal-Wind-Photovoltaic Power System Using BESS to Reduce Wind Power Curtailment

The Daily Operation Scheduling (DOS) gets new challenges while a large-scale of renewable energy is inserted into the power system. In addition to the operation, the power variability of these sources also causes a problem in the hourly pricing, represented here by Locational Marginal Pricing (LMP). Therefore, new applications, such as energy shifting, offer greater efficiency to the system, minimizing the negative effects caused by wind power curtailment (WPC). This paper shows the LMP formation in the DOS of the hydro-thermal-wind-photovoltaic power system with a battery energy storage system and the reduction of WPC. Here, the wind and photovoltaic power plants are designed to be dispatched, not mandatory, to be able to cut the generation, and the insertion of Distributed Generation is considered. Moreover, to solve the DOS problem, the interior-point method is used. Additionally, the DC optimal power flow, used to represent the DOS in addition to the representation of the electric grid, is modeled with an iterative approach. The analysis is made in an IEEE 24-bus system with data from Brazil. Lastly, the results of simulations are presented and discussed, demonstrating the effectiveness of the optimization to reduce the WPC, the total operation cost, and to provide the LMP curve.