Solar PV panels mounted in front of the self tilting trike

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• published: 09 May 2016
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inventortonchev@gmail.com http://www.tonchev.net/ Tel. +359 876 403 727 Energy supply from renewable energy sources is the main goal of our civilization. In planning for energy supply adequacy, determining the optimum level of ancillary services, such as energy reserves is difficult. If an operator maintains too low of a margin of ancillary services, such as reserve margin, there is a high likelihood of being unable to serve the demands of all firm- demand customers. If an operator maintains too high of a reserve margin, financial resources are wasted in building and maintaining capacity that is rarely used. A new digital simulation model is needed that can quantify the risk of occurrence of a wide-range of possible scenarios in terms of expected unserved energy (EUE) and loss of demand probability (LODP). Expensive market purchases required to avoid shedding firm demand can be mitigated over multiple markets with option contracts, storage devices and demand response. A wider range of components that contribute to unreliability need to be modeled than would be in an application that was designed for only minimizing production cost. Almost all reliability issues are expected to occur in the upper forecast errors, hydro forecast errors, and storage availability scenarios. In order to achieve statistical significance, a large number of availability levels need to be included in a simulation that realistically models energy supply, storage, and transportation. The Tree Convolution includes all paths in the tree as need for economic accuracy as determined by a user specified tolerance. The analysis is applied to the energy system model of Figure 1 in the presented embodiment, which is one abstraction of the electric energy supply chain or one instance of a generic supply chain. All renewable energy resources have different data that are required to be input into the simulation model. Operational data includes distributions on outage levels, costs, and capacities, derating information, maintenance information, energy limitations, market prices, and required margins. This data must be collected and correctly input into the simulation model. The digital simulation draws on historical distribution of outages by duration, rather than on an annual or seasonal forecast equivalent force outage rate (EFOR) for each unit. This produces a more accurate reflection of cumulative megawatts forced offline during constrained periods. The digital simulation provides the ability to process hundreds of thousands of iterations of an entire year in a matter of hours. Furthermore, the digital simulation includes dynamic market simulation based on supply/demand, dynamic hydro-operation based on market simulation and hydro-availability, and representation of transmission constraints with load flow modeling. Other features include dispatching supplemental modes of operation for combined cycles, and modeling of capacity reduction based on weather. TAGS: free energy, magnetic motors, DIY solar panel, DIY free energy, hydrogen fuel, renewable energy diy, solar power, wind turbines, hydrokinetic turbine, global warming tips, green energy, tonchev inventions, tonchev patents, fuel cells, perpetuum mobile diy, electric cars, diy electric car, ebike diy, free electricity, window insulation, insulation panes, hybrid cars, range extender cars, electric supercharger, vawt turbine, hawt diy, mechatronic patent, tonchev licenses, hydrogen cars diy, ebike howto, solar pv roof house, energy plus houses, alternative energy solutions, free energy from the sun, free energy at home howto, bio foods patents, solextra tm, pontoon power plants, autonomous submarines