In order to operate a solar photovoltaic (PV) system at its maximum power point (MPP) under numerous weather conditions, it is necessary to achieve uninterrupted optimal power production and to minimize energy losses, energy generation cost, and payback time. Under partial shading conditions (PSC), the formation of multiple peaks in the power voltage characteristic curve of a PV cell puzzles conventional MPP tracking (MPPT) algorithms trying to identify the global MPP (GMPP). Meanwhile, soft-computing MPPT algorithms can identify the GMPP even under PSC. Drawbacks such as structural complexity, computational complexity, huge memory requirements, and difficult implementation all affect the viability of soft-computing algorithms. However, those drawbacks have been successfully overcome with a novel ten check algorithm (TCA). To improve the performance of the TCA in terms of MPPT speed and efficiency, a novel concept of data arrangement is introduced in this paper. The proposed structure is referred to as Optimized TCA (OTCA). A comparison of the proposed OTCA and classic TCA algorithms was conducted for standard benchmarks. The results proved the superiority of the OTCA algorithm compared to both TCA and flower pollination (FPA) algorithms. The major advantage of OTCA in MPPT stems from its speed as compared to TCA and FPA, with almost 86% and 90% improvement, respectively.

The world’s attention has turned towards renewable energy due to escalating energy demands, declining fossil fuel reservoirs, greenhouse gas emissions, and the unreliability of conventional energy systems. The sun is the only renewable energy source that is available every day for a specific period of time. Solar photovoltaic (PV) technology is known for its direct conversion of sunlight into electricity using the photoelectric effect. However, due to the non-linear electrical characteristics, the power output of solar PV cells is bound to a lower value and can not produce the power of which it is capable. To extract the maximum possible power, the PV cell needs to be operated at its maximum power point (MPP) uninterruptedly under numerous weather conditions. Therefore, an electronic circuit driven by a set of rules known as an algorithm is utilized. To date, the flower pollination algorithm (FPA) is one of the most renowned maximum power point tracking (MPPT) algorithms due to its effective tracking ability at the local and global positions. After an in-depth analysis of the design, strengths, weaknesses, and opportunities of the FPA algorithm, we have proposed an additional filtration and distribution process named “Random walk” along with the ordering of solutions, to improve its efficiency and tracking time. The proposed structure named “Ordered FPA” has outperformed the renowned FPA algorithm under various weather conditions at all the standard benchmarks. Simulations are performed in MATLAB/Simulink.

To continuously operate the Photovoltaic (PV) system at its Maximum Power Point (MPP) under changing weather is a challenging task. To accomplish this, multiple MPP Tracking (MPPT) algorithms have been proposed, which can be portioned into two: 1) Conventional algorithms, have the strengths of a simple structure, fewer computations, and low memory requirement, and cheap implementation. Whereas, trapping under Partial Shading Conditions (PSC), steady-state oscillations, and system dependency are the associated drawbacks. Conversely, 2) Soft computing algorithms, perform efficiently under all weather conditions with zero steady-state oscillations, and are system independent. The structural complexities, giant computations, huge memory requirements, and expensive implementation, are the accompanying concerns. The core contribution of this study is to present a deep analysis of all the MPPT algorithms at the standard benchmarks defined in the published literature, for the readers so they could decide which algorithm to choose under certain circumstances.

In this study, we have presented an optimal microgrid design that ensures the uninterrupted energy supply to Mirpur University of Engineering and Technology (MUST), Azad Jammu and Kashmir AJK, Pakistan at the cheapest price by using reliable energy resources. The availability of energy resources, environmental viability, and economic feasibility are the key parameters of design. The available resources for the MUST site include the National grid, Solar photovoltaic (SPV), Battery bank, and Diesel generator. The data of electrical load, solar illumination, atmospheric temperature at the university, diesel fuel cost, SPV module lifetime, SPV degradation factor, SPV efficiency, SPV cost, battery cost, battery life, national grid energy price, load shedding and toxic emissions have been considered valuables in designing the hybrid micro-grid. The difference in net present cost (NPC) of the optimal design and the worst design is calculated by considering the above parameters. The proposed optimal microgrid design supplies energy to the load using SPV, Diesel generator, and battery bank with NPC of $250,546 and the renewable fraction of 99%. Whereas the worst design includes the Diesel generator and battery bank as energy supplying sources with the NPC of $2.14 M and a renewable fraction of 0%. Simulations performed using HOMER Pro software (HOMER Energy, HOMER Pro-3.11, Boulder, CO, USA) proved that after considering all the data and requirements mentioned above, out of 979 feasible designs, the proposed hybrid microgrid design is best suitable for MUST.

Optimal energy extraction under partial shading conditions from a photovoltaic (PV) array is particularly challenging. Conventional techniques fail to achieve the global maximum power point (GMPP) under such conditions, while soft computing techniques have provided better results. The main contribution of this paper is to devise an algorithm to track the GMPP accurately and efficiently. For this purpose, a ten check (TC) algorithm was proposed. The effectiveness of this algorithm was tested with different shading patterns. Results were compared with the top conventional algorithm perturb and observe (P&O) and the best soft computing technique flower pollination algorithm (FPA). It was found that the proposed algorithm outperformed them. Analysis demonstrated that the devised algorithm achieved the GMPP efficiently and accurately as compared to the P&O and the FPA algorithms. Simulations were performed in MATLAB/Simulink.

Extraction of maximum power from PV (Photovoltaic) cell is necessary to make the PV system efficient. Maximum power can be achieved by operating the system at MPP (Maximum Power Point) (taking the operating point of PV panel to MPP) and for this purpose MPPT (Maximum Power Point Trackers) are used. There are many tracking algorithms/methods used by these trackers which includes incremental conductance, constant voltage method, constant current method, short circuit current method, PAO (Perturb and Observe) method, and open circuit voltage method but PAO is the mostly used algorithm because it is simple and easy to implement. PAO algorithm has some drawbacks, one is low tracking speed under rapid changing weather conditions and second is oscillations of PV systems operating point around MPP. Little improvement is achieved in past papers regarding these issues. In this paper, a new method named “Decrease and Fix” method is successfully introduced as improvement in PAO algorithm to overcome these issues of tracking speed and oscillations. Decrease and fix method is the first successful attempt with PAO algorithm for stability achievement and speeding up of tracking process in photovoltaic system. Complete standalone photovoltaic system’s model with improved perturb and observe algorithm is simulated in MATLAB Simulink.