Performance Analysis of Optimal Sized Hybrid Renewable Energy Grid-Connected Systems

Hybrid solar energy-based power generation systems (PGS) are one of the exciting options for potential distributed networks. PV and wind grid linked PGSs are the most appropriate for its good output across various configurations. However, due to the system's complexity, special attention is required to achieve a successful engineering solution in the optimal balance between these two energy sources. This paper discusses optimum scale of PV and wind by following multiple optimization methods to various condition decision analysis (VCDA). The versatility of the VCDA algorithm was tested by taking into account several weighting parameter techniques for differing wind speeds and fluctuations in radiation levels, thus illustrating the advantages and limitations of the suggested optimum size approaches. The subsequent study can be called upon as a significant reference for decision makers, analysts and policymakers.


Introduction
Hybrid renewable energy (HRE) is deemed most critical in the coming days for power generation systems (PGSs) in view of the complexities of increasing renewable energy (REE).
Even as its research continues technologically and economically, HRE PGSs have shown internationally recognized environmental and social benefits. The non-consistent output power for a standalone power generation systems contribution between several options is highly likely to be PV-WP generation systems. Net-connected hybrids have a large potential. The optimal design of these systems requires care, requiring trade-offs between decision-making criteria to improve sustained energy development. Technical literature is rich in proposals on methods for optimizing the size of hybrid PGS. Specific techniques have in the past been used on various methods. The new program will provide a unique technological performance [4]- [10]. PSO [1], genetic algorithms [2] and [3], non-linear, mixed integer programme [4], dual-simulation annealing-tabu search algorithms [5] and specific prospects for dual GMP price optimisation. A certain target feature is assumed in general to be reduced, which mainly is the overall cost of the system; certain technological and environmental criteria may be incorporated in the sizing phase in two schemes: firstly, considering the necessities as restrictions and, secondly, the transfer of the additional method. Therefore, the ultimate judgment on both methods is similarly relevant to all system requirements / variables. With the aid of a Multi-Objective PSO or genetics algorithm the Pareto system is considered to be a great solution collection in [11] and [12] at the same time to boost objective functions (environment, economic or technical). Thus these methods provide the best solution for different PV-WP configurations, thus leaving the decision maker's final choice, which may not be a simple task. In addition, all the parameters in this case are equally relevant. Essence, the right hybrid PV-WP method, a big balance of various parameters in design, contributes to a suboptimal solution cannot be derived from the solutions that are already proposed. The particle swarm optimization (PSO) algorithm was used for optimal location and tuning of a new custom power device (CPD) for minimization of the total CPD injected currents and the total harmonic distortion (THD) of current and voltage. Hence, the real-time control of reactive power with CPD was suggested. The PSO method was proposed to find out the optimal size and location of the distributed active filter system for reducing total losses while satisfying harmonic voltages, THD limits on a typical 37-bus distribution system [13]. In [14] optimal location of UPQC for enhancing the power quality in distribution network under critical situations has been investigated. Cuckoo Optimization Algorithm is proposed to find the optimal placement and number of UPQCs for improving the power quality issues. Reconfiguration system and placement of UPQC were used for power loss reduction and maintaining voltage stability in a distribution network with different evolutionary algorithms [15]. Then, a steadystate model of UPQC was used for the forward/backward sweep load flow. Distributed power condition controller (DPCC) with the fuzzy based PI controller was proposed in [16] to enhance power quality in a multi-microgrid. The relative capacity credit of the renewable power plants is typically 25-50 percent. The intermittent renewable sources and loads in grid cause many negative problems in these networks.
In this article the VCDA approaches are used to solve some of the above limits so that the optimal size share can be defined among PV and WP plants. The suggested solution helps one to hit the optimal level by simultaneously implementing various criteria (technique, economic, environmental or social), without trying to transform it into a single entity. The responsiveness of the proposed algorithms has therefore also been evaluated in the light of the various weighting parameters and specific variance circumstances based on speed of wind and radiation emits from solar system. This can be used during either a new PGS hybrid design or the assessment of various unusual development for an accessible system. The following design models are described in Section II. Section III explains the VCDA and its suggested approach to optimization; Section IV provides the findings of a realistic argument for research and, ultimately, its implications are summarized in Section V.

Modeling and Concept systemic methods
This segment shows the numerical models taken for various PV-WP size configurations and theoretical design constraints. PGS aims primarily to meet cargo demand and enhance sustainability. If ample HRE sources are available then zero economic interest is used for the additional energy produced after meeting demand for price. The HRE plant is expected to respond best to the demand curve and therefore will not provide local power supplies with extra energy at a competitive rate. On the other side, if renewable energy reserves are small, the electricity shortfall is faced by the grid. Therefore, the load requirement factor is an essential input to the formulation criterion and is measured as recognised system data.

PV System Model
The developed power P PV can be analyzed by the following expression: Though open circuit voltage and short circuit current were depend on the required temperature T C and the universal irradiance R G . The numerical relationship was expressed by the following equations:

Wind Power Generation Systems
The amount of wind power can be developed by its speed variation and can be expressed using [12].
The overall power generated by WP is computed by .max .
The aforesaid numerical models of PV and WP were utilized to forecast and run the developed power linked to variable PV-WP generation system.

Design Criteria
This paper chooses to optimize the various design criteria, which reflect the ecological, cost-effective and social outline of the suggested ecological criteria -C 1 emission reduction. The reduction of atmospheric pollutants from SO 2 and NOx emissions from HRE sources to accomplish the load is estimated in ton / h emissions rather than in fossilfueled thermal units [11].

Performance Criteria-Estimated Costs (C 2 ):
This performance condition is measured as the amount of installation expenditure, operating and repair, and electricity from system costs minus the recovery benefit of photovoltaics or transmitting systems. The following equations are used for the calculation of this performance criterion, labeled EC [11]:

Social Criteria-Social Acceptance (C3):
In this sense, the usage of the land and its visual effects have been taken into account, including, social impact evaluation phase, electromagnetic interference, acoustic disruption, flicker shades and habitat disturbance [15]. The use of the land involves social opposition to the deployment of the Hybrid PV-WP generation systems.
The social requirements methodology is carried out in this report using a fugitive logical algorithm, which reveals the input variables being the land area used in PGS and the amount of WP required, while the performance of this algorithm represents an indication of social approval. The input and output quantity membership functions displayed in Fig.   1. Table I provides relevant fuzzy guidelines. In this article, the lesser number of WPs to fit the power demanded is provided greater priorities in installing PGS.  In the projected case study, the profiles shown in Fig.1 were used where the minimum installed PV capacity was set at a level up to 50 kW; wind power generation systems of 10, 30 and 50 kW have also been considered, and the arrangement of three wind generator turbine sizes consent for the maximum of possession as the least number of WPs is forced. With regard to calculations of land use, 1 kW installed photovoltaic power requires a maximum of 10m 2 [16], though essential land for the wind power generation system is measured in accordance with installation regulations and thumb rules. PGS includes the minimum land needed for PV and WP. Models for social analysis of requirements may vary from place to place since embracing or rejecting local populations relies heavily on their group.

Design Constraint
The overall energy intake from the HRE method is limited to a minimum by enforcing that certain quantities shall not be more than a certain threshold level for the pre-defined evaluated duration T, presumed to be 8450 H ( per year). These are the following parameters: In conjunction with the energy efficiency policy implemented by the Network, the net energy generated from excess power is sold to the grid. The suggested optimization strategy also views excess electrical electricity as unjustified added expenses and, regardless of the added infrastructure built, societal acceptability fines, these must be reduced. The importance of threshold depends very promptly on the output of PGS and is 0.5% of EPGS were considered.

SIZE OPTIMIZATION OF PV AND WP GENERATION SYSTEMS
The suggested technique uses various condition and decision analysis VCDA to achieve an optimum size of PV-WP systems in accordance to a variety of parameters, as stated in the introduction. VCDA approaches equate two or more choices with two or more parameters where each parameter has a given weight in its final judgment. In the following equation [17], the question could be formulated as: X ij is the value of i th parameter adjacent to the j th condition and calculated by PV-WP simulations in this analysis. In order to solve multicriteria issues, as outlined in the previous section, the definition of criteria and alternatives is necessary and the weights of criteria and performance matrix will also be required.

Weighting Techniques
The suggested design method takes different weighting techniques to allow sensitivity study of the outcomes. These methods are briefly described below. Smarter is a discretionary option that tests model strategy, depending on its experience and interests.
Entropy is a goal weighting method based on X which results in higher weight values if a larger discrepancy between the column of performance criteria (alternatives) has been achieved. For implementing Entropy, the following steps [19] are necessary.
i) P ij can be estimated by using ii) E j can be estimated by using iii) d j can be estimated by using dj=1-Ej (25) iv) b j w can be estimated by using Two different ways of combining the above-mentioned weighting methods are adequately followed:.

Sources sizing algorithm
The HPGS by an HG using PV and WP is modelled as given belows:  where α, β, γ and ρ are constant factors and n is required interval. The instant error among generation and load can be computed by: where Δp shows the instant error. The overall instant error were represented by ΔP and computed as ( , ) ( , ) A smaller cumulated failure value indicates that an unreliable generation efficiently meets the need for the load whereas a larger combined failure value implies that the difference between the blended generation of renewable energy and the need for load is large. The accumulated error is estimated and processed in a matrix as follows for any conceivable PV / WP combination: The logic of SSA is presented in Fig.4.

Results and discussions
The variations of solar PV and WP power are shown in the Fig.6. As previously stated, efficiency and costs are the key criteria for the evaluation of microgrid output. It should be remembered that a mixture of WP and PV is given for increasing solution vector array, and total installed ability of RE sources is improved by an improvement in the array. The optimum combination is the index with an optimal decision variable value. Due to the higher value of the optimum decision variable, the reliability at reasonably low cost is comparatively higher while the lowest value of the optimal decision variable shows a high reliability at high costs or low reliability at lowness. The combination PV and WP, which correlates to the SV index number 187, is the optimal answer from Fig.5. The PV and WP power is respectively 57 MW and 187 MW. The limit and then the patterns for the optimum judgment variable value were found at the outset. Lower ODV values at the onset are attributed to a smaller volume of energy supplied in PV and WP power. Similarly, ODV values are also small for higher indices, because costs are very high with largely installed capacity, making the solution un-economic. Although its impact on ODV is higher (service of energy) compared to costs, as the expense of RE sources and storage is high and overall cost becomes even higher for very large capacities, which makes a solution very expensive. In addition to this, the Figs.6. It may also be noticed that the exactly minimum and maximum PV and WP capacity limitations must be chosen though the optimum size of each method is determined. The limitations can cause the algorithms to operate in a region that has low ODV values and leads to a solution that is uneconomic. As stated previously, the RE sources' production is erratic, hence it may happen, during service of the HG, that the Reg performance is inadequate to satisfy the demand needed, in these situations the HG purchases energy from the utilities grid to transfer the load. The Fig.5 shows the overall energy transferred by the HG and the utilities grid to reach the necessary load. It is seen that the change in indexes raises the energy given by the HG while the energy generated by the power grid declines. At first, HG-supported electricity decreases and the grid capacity declines both saturated and heavy. The increase in demand and generation is seen over the span of one year in Fig.6. The supply can be shown to still be the same as demand. As the analysis network is linked to the grid and the power grid acts as a buffer. Therefore when the production capacity of HG is not appropriate for supplying the necessary load demand, HG acquires power from the grid, which renders the overall device extremely stable. In Fig.7 presented that the hybrid fluctuations of the WG and PV power generation were effectively controlled below the 10 percent limit for the 10 minutes using method 2. The results above show that using the control method 2 the rate of power fluctuations in the specific ranges can more efficiently be regulated. It should also be noted that the filter time constant needs to be modified and updated timely because of the inertia feature of the first order filter, otherwise sometimes the power fluctuation limit is difficult to ensure. It is important that this method (Method 1) is a function of the variable time constant checks strategy. Method 2 is however not only more easy to apply, but also can ensure the efficient control of the power fluctuation rate within a limited range once power fluctuation limit value is provided. Moreover, the power fluctuation rate limit value will be regularly updated on the basis of operating conditions of the specific location. It is also suggested.  The algorithm proposed also does not require parameter tuning, like traditional optimization approaches focused on impact and testing methods. Table 2 identifies the different potential approaches to demonstrate the efficiency, based on unit cost, the energy provided by renewable energies (energies provided by HG) and CO 2 emissions, of the suggested methodology.  Table 3 indicates that the rise in the renewable energy percentage given by switching from Cases II to III equals the decreased expense percentage. The percentage reduction in CO 2 emissions is also rising, counter to the investment increase.
Case III levels are approximately 70% lower than those for Case I, i.e. modern decades. Cases III CO 2 levels are also less than Case III. Case V has the lowest CO 2 emissions, but in this case, the power of RE and BESS sources for most is very high per units of expenditure. The cost for each product in Case I V remains higher and CO 2 pollution decreased. The above discussion clearly shows Case III to be an excellent solution because it offers considerable clean energy and reduces CO 2 emissions significantly per unit charge, namely 14.27 c / kWh at a reasonable cost.
It should be remembered that cost is the aspect / function of several other factors , for example where solar irradiation, wind velocity or load curve is more accurately associated, contributes to a more cost reduction, which renders our optimized approach stronger and more rational.
MATLAB is utilized to model and simulate. Simulation is conducted on the core i7 6th generation RAM platform, 2.6 GHz, 16 GB.

Conclusion
A technique to optimize the capacity of RE sources e.g. was proposed in this paper. The optimal capacity is measured on the basis of the energy supplied per unit cost ratio. The ideal method has been shown to be inexpensive and to contain less emissions of CO 2 . A cost-and pollution analysis is often made under some chosen situations, and the best alternative is proven to be preferable to the other alternatives. The value of the approach suggested resides in ensuring that it will not over-and under-size as all feasible alternatives are found. Moreover, forced outages of WP are considered to make the methodology more practical. The suggested approach is very general and can be extended to different types of generation and storage technologies and to other geographical locations.
Compliance with Ethical Standards 1. Disclosure of potential conflicts of interest: The authors declare that they have no conflict of interest.

Research involving human participants and/or animals: This paper does not contain
any studies with human participants or animals performed by any of the authors.

Informed consent:
Informed consent was obtained from all individual participants included in the study.

Funding:
This study was not funded by any agencies.