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Cogeneration is preferred mostly in process industries where both thermal and electrical energies are required. Cogeneration plants are more efficient than utilizing the thermal and electrical energies independently. Present government policies in India made renewable energy generation mandatory in order to minimize fossil fuels consumption and to protect the environment. Hence, many cogeneration plants have been integrated with renewable energy generation. However, post-integration effects increase and introduce inefficiencies in the operation of cogeneration systems. In this paper, a case study of an identified typical cogeneration plant where renewable energy is integrated is considered. Post operational effects on the plant due to integration of renewable energy (solar) are studied and by practical experimentation through cost-benefit analysis the break-even point beyond which renewable energy generation introduces inefficiencies is estimated. Next, a systematic methodology is developed based on the heuristic forward-chaining approach technique to establish the breakeven point. An algorithm/flow chart is developed using an iterative method and executed through MATLAB using practical data from the industry. Suggestions for suitable energy storage devices to store renewable energy beyond the breakeven point, based on a techno-economic analysis of energy storage technologies, are made. Further, the battery energy storage system is designed and the capacity is estimated based on the practical solar irradiance data. A rule-based algorithm is developed to control the charge and discharge cycles of battery storage based on predefined conditions. The payback period is estimated based on the expected monetary benefits of proposed energy storage and the economy of the proposed system is ensured. The post-operational issues are resolved by introducing energy storage. The methodology presented in this paper can be a guiding tool for optimization of various renewable-energy-integrated cogeneration systems. [ABSTRACT FROM AUTHOR]

By considering the weight penalty of batteries on payload and total vehicle weight, this paper shows that almost all forms of land-based transport may be served by battery electric vehicles (BEV) with acceptable cost and driving range. Only long-distance road freight is unsuitable for battery electrification. The paper models the future Indian electricity grid supplied entirely by low-carbon forms of generation to quantify the additional solar PV power required to supply energy for transport. Hydrogen produced by water electrolysis for use as a fuel for road freight provides an inter-seasonal energy store that accommodates variations in renewable energy supply. The advantages and disadvantages are considered of midday electric vehicle charging vs. overnight charging considering the temporal variations in supply of renewable energy and demand for transport services. There appears to be little to choose between these two options in terms of total system costs. The result is an energy scenario for decarbonized surface transport in India, based on renewable energy, that is possible, realistically achievable, and affordable in a time frame of year 2050. [ABSTRACT FROM AUTHOR]

With the technical growth and the reduction of deployment cost for distributed energy resources (DERs), such as solar photovoltaic (PV), energy trading has been recently encouraged to energy consumers, which can sell energy from their own energy storage system (ESS). Meanwhile, due to the unprecedented rise of greenhouse gas (GHG) emissions, some countries (e.g., Republic of Korea and India) have mandated using a renewable energy certificate (REC) in energy trading markets. In this paper, we propose an energy broker model to boost energy trading between the existing power grid and energy consumers. In particular, to maximize the profits of energy consumers and the energy provider, the proposed energy broker is in charge of deciding the optimal demand and dynamic price of energy in an REC-based energy trading market. In this solution, the smart agents (e.g., IoT intelligent devices) of consumers exchange energy trading associated information, including the amount of energy generation, price and REC. For deciding the optimal demand and dynamic pricing, we formulate convex optimization problems using dual decomposition. Through a numerical simulation analysis, we compare the performance of the proposed dynamic pricing strategy with the conventional pricing strategies. Results show that the proposed dynamic pricing and demand control strategies can encourage energy trading by allowing RECs trading of the conventional power grid. [ABSTRACT FROM AUTHOR]