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Integrating Distributed Energy Resources (DERs) and Micro-Grid (MG) into a system evolved the traditional power system. In spite of their significant advantages, MGs may result in volatility and uncertainty in the power systems. For reliable operation of the grid, energy trading among MGs should be optimized to maintain a fair trading price, maximize participants’ profit, and satisfy network constraints. In this paper, the optimal power trading among multiple reconfigurable MGs is formulated as a Mixed-Integer Nonlinear Programming (MINLP) considering all energy resources and their dynamic prices. In spite of the other methods in the literature, the proposed method minimizes the total cost (increase sales and decrease purchases) and transmission loss considering all energy resources in the MGs. In order to flatten the load profile, a time-based load profile is considered for the demand response program. The performance of the proposed model is evaluated on an IEEE 6-bus network as well as a modified IEEE 33-bus test system. The results verify that the proposed method, (i) determines the best configuration among MGs with a switching reduction of about 30%, (ii) optimizes the power generation of energy resources with 12% reduction in energy production, and (iii) optimizes the power trading costs with a 10% reduction in costs compared with the basic model without DR and trade that is introduced as $Scen.1$ in this paper.

Of having immense regional capacity of 395,096 MW as of mid-2018, South Asia is gradually emerging as a fulcrum of electricity exchange and powers trading so, bilateral exchanges are occurring, as evident in the noteworthy Bhutan-India power flow of 1,410MW. India and Bangladesh have four historic power trading practices in place and the Power Purchase Agreement of 2014 between Nepal and India, these two countries exchange up to 350 MW of electricity. All these have triggered immense possibilities opening the scope for multilateral power flows. A huge jump from the present total cross-border trading of hardly 2500 MW is very possible. Nepal could potentially be the biggest beneficiary in this game. If harnessed steadily, its power could be sold across South and South East Asia, with wheeling facilities provided by Indian national grids. A Bangladesh–Bhutan–India trilateral hydroelectric power-generation agreement is likely to be signed soon. Energy secretaries of Bangladesh and Nepal have decided to develop hydropower projects in Nepal through government-to-government investment and then export the electricity thus produced to Bangladesh through the Indian transmission system Power trade would change the composition of the export baskets of power exporting countries and help them address their adverse balance of trade and balance of payment. Additional income from power export and an enhanced level of economic activity can be invested in social infrastructure.

This study investigates the energy cost and carbon emission reduction problem in geographically distributed cloud data centers (DCs), where each DC is connected with its own renewable energy resources (RERs) for green energy generation. We consider four cloud DCs that are operated by a single cloud service provider. They consume energy from both RERs and from the commercial grid to meet the demand of cloud users. For energy pricing, we consider four different energy markets that offer varying energy prices per hour. Additionally, our proposed strategy enables DCs to sell excess electricity to the commercial grid in peak-price hours and purchase in low-cost hours according to power trading. This work also exploits energy storage devices (ESDs) to store energy for future use. We utilize real-time data requests, weather data, and pricing data for performing simulations and results affirm the effectiveness and productiveness of our proposed method to mitigate the energy cost and carbon emission of cloud DCs.

Demand side management (DSM) is one of the most challenging areas in smart grids, which provides multiple opportunities for residents to minimize electricity cost. In this work, we propose a DSM scheme for electricity expenses and peak to average ratio (PAR) reduction using two well-known heuristic approaches: the cuckoo search algorithm (CSA) and strawberry algorithm (SA). In our proposed scheme, a smart home decides to buy or sell electricity from/to the commercial grid for minimizing electricity costs and PAR with earning maximization. It makes a decision on the basis of electricity prices, demand and generation from its own microgrid. The microgrid consists of a wind turbine and solar panel. Electricity generation from the solar panel and wind turbine is intermittent in nature. Therefore, an energy storage system (ESS) is also considered for stable and reliable power system operation. We test our proposed scheme on a set of different case studies. The simulation results affirm our proposed scheme in terms of electricity cost and PAR reduction with profit maximization. Furthermore, a comparative analysis is also performed to show the legitimacy and productiveness of CSA and SA.

In this article we propose a new wind power forecasting model that does not focus on providing the most precise forecasts, but minimizes the financial loss of forecasting impreciseness. We show that the loss function is asymmetric and therefore account for asymmetry during the estimation stage of our model. The new model's forecasts are compared to two state-of-the-Art models and we are able to show that the new model can increase the financial profit for power producers, power traders and/or network operators by a severe degree.

Electricity price forecasting in deregulated open power markets using neural networks is presented. Forecasting electricity price is a challenging task for on-line trading and e-commerce. Bidding competition is one of the main transaction approaches after deregulation. Forecasting the hourly market-clearing prices (MCP) in daily power markets is the most essential task and basis for any decision making in order to maximize the benefits. Artificial neural networks are found to be most suitable tool as they can map the complex interdependencies between electricity price, historical load and other factors. The neural network approach is used to predict the market behaviors based on the historical prices, quantities and other information to forecast the future prices and quantities. The basic idea is to use history and other estimated factors in the future to "fit" and "extrapolate" the prices and quantities. A neural network method to forecast the market-clearing prices (MCPs) for day-ahead energy markets is developed. The structure of the neural network is a three-layer back propagation (BP) network. The price forecasting results using the neural network model shows that the electricity price in the deregulated markets is dependent strongly on the trend in load demand and clearing price. � 2010 Elsevier Ltd. All rights reserved.

This paper investigates the economic attractiveness of exporting coal-generated electricity from the State of Wyoming to the State of California while investing in large-scale CO 2 capture and sequestration to meet California's strict environmental regulations. The project involves partially capturing CO 2 from coal power plants, storing it temporarily underground using CO 2 Interim Storage, and ultimately using it for Enhanced Oil Recovery. A detailed financial assessment is performed in view of current electricity prices and federal and state regulations in the U.S., especially those related to CCS tax credits. The results show that the project is profitable under current regulations and technical assumptions, and it can be a good opportunity to drive large-scale deployment of CCS, along with the development of EOR activities and the deployment of the newly proposed CO 2 Interim Storage technology.