A microsystem is basically a local island network that can function as a stand-alone or network-connected system. It is powered by gas turbines or renewable energy sources and includes dedicated converters and interconnections to connect to an existing network. Special-purpose filters overcome harmonic problems while increasing the quality and efficiency of electrical power.
In short, we are thinking of building a micro-network as a local power provider with limited advanced management tools where the smart grid is a broadband provider with sophisticated capabilities to support automated decision-making. An example of our microsystem that is subjected to our experiment is shown in Figure 2. If this serves the municipal energy centre [ 3 , 4 ], then the following issues need to be considered for micro-network activity: Independence of urban infrastructures mobile electricity infrastructure, gas infrastructure, water systems, waste recycling, wastewater treatment.
Restricted RES penetration, which cannot be considered significant in cities where micro-networks exist as they are known and defined. It can be said that the first issues are that infrastructure and urban systems are viewed as individual [ 7 ], that is, transport, sewage and water supply, which are usually highly interactive and interdependent Figure 3. In order to ensure the security and reliability of the power supply from RES and the network, electrical resources must be planned and effectively controlled. The large distribution network consists of many elements including generators, transmission lines, transformers and circuit breakers.
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In addition, market structure and real-time energy pricing need to be assessed. For stable operation of the micro-network, it will be necessary to plan electricity generation and supply it to the system load for every second of the intelligent area operation—Rohan Island Prague 8, Czech Republic see Figure 1. Energy sources for large energy systems consist of water, nuclear energy, fossil fuels, renewable energy sources such as solar energy photovoltaic systems as well as green energy such as fuel cells, biomass and combined heat and power CHP or also known as cogeneration.
These resources must be managed and synchronised to meet the load demand of the microprocessor. The load requirement of the RES and electricity grids is cyclical and has a peak daily demand for hours and minutes of the week, that is, weekly peak demand for each month and monthly peak demand for the year.
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Figures 4 and 5 show the course of electricity demand in our intelligent area on Mondays and Thursdays. The energy resources must be optimised to meet the peak demand of each load cycle, so that the total cost of generating and distributing electricity is minimised.
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The power system operator must plan the power sources of the grid and equipment to meet the different load conditions. Systemic load has a general mathematical formulation. This load gradually increases during the day and then decreases during the night. The cost of the generated power of individual RES sources is not the same for all sources.
Therefore, there is a higher effort to produce more energy at the least cost in units.
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In addition, several network lines connect one electrical network to another neighbouring power grid. These are called interconnections between networks. When exporting power from one power system to an adjacent power supply system through a connecting line, balanced power is considered a load; and conversely, when such power is imported, it is considered energy production.
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Flow control through these network distributions is preprogrammed software based on safe operation and economic indicators. In order to control the energy flow through the connection lines within the transmission at a given frequency of the system, the concept of the control error is introduced area control error, ACE and is defined as [ 10 ]. Distribution of changing loads between generators minimising operating costs. The above conditions are subject to additional limitations that may be introduced in network security considerations, such as loss of power in the line or in the generator.
The first objective is to solve the additional controller and the distortion concept. In the case of a small change in load on the microsystem in the intelligent area, it leads to proportional changes in the system frequency. In order to achieve reasonable regulation i. ACE reduced to zero , system load requirements are sampled every few seconds. The second objective is to fill energy consumption in the prescribed sample at each minute and to allocate the varying load between the different units to minimise operating costs. This assumes that the load demand remains constant over each period.
Figure 7 shows the AGC block diagram.
The AGC also manages the connected micro-networks in a large interconnected power grid. The microgrid concept assumes the grouping of loads in the area within various micro-projects, such as photovoltaics, biomass and combined CHP, acting as a single control network. For the local grid, this cluster becomes the only discernible burden. When the micro-network is connected to the grid, microprocessing voltage is controlled by the local grid. In addition, the frequency of the electrical network is controlled by the operator of the electrical network.
The microgrid cannot change the voltage of the bus network and the frequency of the power supply. Therefore, if the microwave network is connected to the local grid, it becomes part of the network and is subject to network failures. The AGC control system is designed to monitor system load fluctuations. The economic supply is expressed in a mathematical process where the required electricity production from the grid including the RES within the micro-network of the intelligent region, Rohan Island, is divided between individual energy sources within the operating RES micro-networks, and thus by minimising defined cost criteria [ 4 ], it is subject to both load and operating constraints or penalties.
For each specified load over time see Figures 4 and 5 the power of each RES power plant, including electricity from the distribution grid i. The problem of economic supply is traditionally formulated as an optimisation with quadratic cost objective functions [ 11 , 24 ]:. If independent variable P g function argument P gi , that is, power of the IT resources in time t and x i t , is the energy state of the IT source over time t , we get the basic relationship of the cost function:. Support is also available [ 8 ].
We will separately allocate the operation and sorting of RES costs. Where Ng is the number of resources in the network and T the number of times or slices are considered during the day 24 hours. This relationship is written without expressing the cost.
The individual variables cost items mean C is cost-dependent on the power output e. Restrictive conditions include balances and imbalances according to energy algorithms, as well as generator, bus, voltage and current flow limitations. We use alternative methods such as evolutionary programming EP [ 12 ], genetic algorithms GA [ 13 ], taboo searching [ 14 ], neural networks [ 15 ], optimisation of particle flocks [ 16 , 17 ], the stochastic optimisation algorithm simulated annealing SA [ 18 , 29 ] and adaptive dynamic programming ADP which are passive learning methods to improve the performance of the economic delivery algorithm.
In the overall RES resource management hierarchy [ 11 ], resource deployment is coordinated with the planning of economic supply and maintenance and production over time. While respecting constraints and unexpected stochastic variables, certain assumptions are made when compiling a mathematical statement of resource sorting. These may include, for example, rotary reserves of electricity currents, equipment for respective initial reserves under the conditions of a boiler in the case of biomass or partial formulation with the commencement of operation.
The first constraint is that realistic electricity production must be greater than the sum of the total electricity consumption power of consumers in the intelligent Rohan Island area, including required power reserves, or the sum is equal to. P cel is the total required power net demand [MW]. The RES micro-network should maintain a certain power reserve; then the cap of the power reserve must be modified in some way. P pop is the production power demand in [MW], P zt is production power loss in [MW], P gi is the total real electricity production in [MW], and P gi res is the total reserve of electricity actually produced.
Resource sorting belongs to the classic Lagrange relaxation technique, but the solution to the constraints is based on stochastic variables. That is why we have solved optimisation by simulated annealing as stochastic optimisation we will not deal with this further; see [ 8 ]. Allowed cost functions are conditional when the output power produced by the local network micro-networks of RES in the given hour A t is determined by the sum of resources turned on. We draw from a typical daily electricity consumption diagram at any given time.
The optimisation algorithm works with acceptable solutions see [ 8 ] which can be evaluated through cost functions without the use of penalties. The design of special-purpose function f x is one of the most complex steps of optimisation. There is no guide or procedure of creating such a function.
If we are to design such special-purpose function, we have to know what we are to achieve and what the starting point may be. When we consider our problem, we can see that, in order to achieve reliable and functional results, we have to solve it using constrained optimisation. The constrained optimisation may then be mathematically expressed as follows:. Furthermore, f , g i a h j has the functions, and I and J has the final sets of indices. From a general point of view, the optimisation problem can be expressed as follows:.
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The special-purpose function can be expressed as a sum of quadrates of the deviations between the current parameter values and the required values. The value of the minimised special-purpose function or the value of the optimised system parameters depends on the status vector:.
The procedure of its optimisation is then as follows:. If our problem is formulated from the point of maximisation, then it is easy to make the adjustment to minimise. In that case, the situation would be the following:. The special-purpose function design is a very complex problem, requiring considerable experience in the subject area, and the possibilities of defining optimisation must be considered. We need to build on what is to be achieved and what can be done. We have based our experience and our research on optimisation solutions of energy systems from energy companies within the Czech Republic.
Based on this we have described the physical model of the energy grid, RES microgrid Figure 2 , which corresponds to our experiment.
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