Optimization of Microgrid fed from Hybrid Energy Sources using the Swarm Intelligence

Since the growth of the use of renewable energy resources and the installation of new renewable-based power plants is a global focus shift towards a sustainable society, with all the preplanned new installations, it is imperative to use renewable energy with the highest efficiency and reliability that technological advances can make possible with the integration of Micro grid. It needs control and management at different levels to allow the inclusion of renewable energy sources. This area of research presents a micro-grid fed from wind and solar based renewable energy generating sources [1]. Electric power grid has been in existence and operational for more than ten decades. Thermal power plants which use coal as fuel contributes a large part of the power generated [2]. The cost incurred in transportation of coal is tremendous and an easy way to optimize this is to install thermal power plants near the coal mines. Hydro power generation too is limited to a specific location due to constrained resource availability. Even though power generation is specific to a location, the end user or the consumer is distributed throughout the country. The need for high voltage transmission lines is essential for interconnection of various generating stations and thus the conventional power system consisting of bulk generating stations at specific locations, high voltage long distance overhead transmission lines and low voltage distribution side emerged [3]. HVAC lines, and rarely HVDC too, carry the bulk power generated at one end to the other end of the country, where it is distributed to consumers at a lower voltage level. The changes in generation and consumption are reflected throughout the grid and such a system is called a synchronous network with centralized generation [4]. The advantage of a synchronous system is that the frequency of the voltage in the network is a single parameter that indicates the stability of the grid. The main strategy involved in power system control is balancing the autonomously fluctuating demand with variable generation, failing which the wastage of resources and damage on economy will be huge [5]. If generation and demand are balanced then the power system frequency will be nominal. If the demand tends to increase further keeping the generation constant, the frequency will decrease and vice versa. In traditional power system, Automatic Generation Control (AGC) and Load Frequency Control (LFC) are identified as means to

control action. The control could be usually adjusting the steam pressure by burning more coal or increasing the water inlet pressure in case of a hide plant. The scheme implemented should be able to meet the fast changes in frequency deviation, but in case of thermal power plants the response is slow. During extreme conditions if the mismatch between supply and demand is widening, utility is left with no option other than load shedding [7]. The demand is time varying in nature and if the generation fails to meet it with AGC and LFC [8], the operator has to go for load shedding in order to avoid under-frequency tripping. When it comes to uncontrolled and intermittent generation schemes like wind and solar, AGC is not in the picture and even the load shedding will demand a timing and a volume that match the balancing requirements on the grid. The demand for electricity has expanded at a rate that has outpaced the increase in capacity as the power grid has developed over the years. Wind and solar power integration, among other RE technologies, shows promise. Due to their dispersed, low-power-density natural environments, all of these resources must be connected to the grid in order to have a significant effect [9]. To meet this need, the idea of distributed generation arose, which allows for the integration of renewable energy sources (RE) even at the low voltage distribution side of the power utility network. Microgrids were conceptualized in part because to distributed generation (DG) systems that could link to an existing power grid. A micro grid is a miniature power system, which has local generation and consumers spread over an area of a few 100 – 1000 km2. The power flow in a micro grid is bidirectional when it is interfaced with the main grid, as it can aid the main grid at times with local generation in excess of local demand and absorb power from the main grid when there is a local deficit [10].This study is arranged as follows: in part 2, a description of the literature review; in section 3, a description of the research gap analysis; in section 4, an analysis of the results; and in section 5, a description of the final conclusion.

In [11] author considered data centers as sources of flexible demand for DR. Data centers are used by tenants on a tariff that is fixed based on the usage of the service. The strategy considered that data centre operators are participating in DR and proposed an algorithm to effectively address DR, which involved scheduling of tenants of data server. In [12] author described for DOE suggested an advance version of DR, named as Demand Dispatch (DD). The introduction of distributed generation/resources catapulted this advancement. DD primarily focuses on demand side resources, which included consumer loads and local generation in the In [13] author evaluated the true potential of DD in DSM. The authors identified DD to be an efficient adaptation of DR, facilitated by the advancements in communication technology and smart grid. Traditional DR is deployed for reduction of peak consumption, whereas DD is deployed for 24×7, throughout the year. The authors also identified a set of potential DLs. Dishwashers, cloth washers and dryers, water heaters, HVAC with thermal storage are a few among the suggested loads. The time response of these loads could be in terms of seconds to few milliseconds. The authors evaluated the scope of DD with smart charging of Plugged in Electric Vehicles (PEVs) in maintaining grid frequency. The article also emphasized on the role of smart appliances and IoT in aiding DD. In [14] author investigated the possibilities of super capacitor based energy storage for wind power smoothening. The proposed strategy was suitable for short-term corrections in predicted wind power; however, authors suggested long term storage to be essential for huge deviations. In [15] author evaluated the charging/discharging effects of EV using renewable sources as DG sources. In [16] author investigated the possibilities of scheduling the charging/discharging of EVs to balance intermittent wind power generation. In [17] author established that the co-ordinate operation of wind generators and EVs can yield primary frequency control in micro grids with high wind penetration. In [18] author proposed a grid-to-vehicle (G2V) charging methodology incorporating optimization techniques to reduce the imbalances induced by wind. In [19] author proposed a co-ordinate control of LFC for thermal generators and scheduled charging of EVs to maintain supply demand synergy. A power allocation layer and a control layer for BESS to reduce wind power fluctuations. In [20] author puts serious operational restrictions like active power control on wind farms, due to un-schedulability and intermittency issues. When high wind penetration is anticipated in the near future, appropriate modifications are to be made in the grid code. One such modification could be the introduction of DD programmers and dynamic tariff. The major challenge to be addressed in DD is the customer participation. The motivation for participating in a DD or DR program is the incentives associated. For DD/DR schemes to succeed, the their consumption, which is pretty hefty. In [21], EV charge/discharge control is suggested depending on the SoC, departure time, and other features of the EV. This DR method mitigates frequency fluctuations in wind-powered microgrids. Similarly, [22] has employed EV scheduling based on Model Predictive Control (MPC) with scenario optimization to reduce risk associated with wind power injection. This study uses an enhanced Particle Swarm Optimization (PSO) method to tackle the large dimensionality and complex restrictions in optimal functioning of the various EV charging systems described by [23]. Wind power injection is utilized to optimize the size and operations of battery energy storage systems (BESS) for compensating wind power production changes, although BESS placement is not taken into account. Flexibility in operation is what elasticity of an appliance amounts to in the planned household DR incorporating elastic and inelastic equipment [25]. Each home's optimal contribution to the system is communicated by the aggregator, and from there, the homeowners make operational decisions independently. In [26]'s planned DR for homes, the loads are broken down into three groups: those that can be interrupted, those that can be adjusted, and those that can be shifted in terms of time. One of the loads that might be moved is an EV. Each load type's operating limitations are simulated so that they may be accommodated by DR in a microgrid. Smart house energy management is suggested as part of yet another residential DR programmed in [27]. Smart home technology, cogeneration, and battery storage are all part of the energy management system. A has addressed the issue of decreasing peak loads. The authors of [28] took into account data centers as adaptable demand generators for DR. Tenants pay a flat rate for using data centers regardless of how much data they store. Data centre operators' participation in DR was taken into account, and a suggested method for efficiently addressing DR was presented; this algorithm required the scheduling of data server tenants. An improved form of DR called Demand Dispatch (DD) was proposed by DOE in [29]. This progress was propelled by the use of distributed generation and resources. Demand response prioritizes demand side resources such rooftop solar photovoltaic (PV) and micro wind turbines for local generation in addition to consumer loads. Because of developments in communication technology and the smart grid, the authors found that DD is an efficient adaption of DR. While traditional DR is used to mitigate consumption spikes, DD is operational around the clock, all year long. A group of possible DLs was also found by the authors. Recommended loads include dishwashers, clothes washers and dryers, water heaters, and heating, ventilation, and air conditioning systems with thermal storage.

The issues associated with high wind power injection and solutions suggested in published literature are thoroughly studied. It is observed that majority of the DSM schemes do not embrace the concept of DD effectively. The scope of DD in smoothening the variations in RE sources is potentially high. The operation of DLs leading to efficient DD operation is another task that needs to be resolved. Optimizing DL control without sacrificing the operational constraints of grid is yet another challenge [36-49]. Maximizing RE utilization is the prime goal for utilities in realizing green energy concept. However the uncertainties in RE generation force utilities to employ RE curtailment which weakens green energy initiatives. DD can ensure smooth operation of RE sources, as the instabilities in generation can be balanced through DLs. On the demand side, a few large residential consumer appliances, several

machines, huge battery chargers, plug-in electric cars, water heaters, etc., all qualify as heavy loads. Unless the appliance itself is a smart appliance, DD on appliances/DLs requires a smart controller. There are plenty of the DLs described above, but they don't pack much of a punch. In addition, the accessible DLs will be widely spread over the grid, since DD is still a novel method for which no established frameworks exist. It will be difficult for utilities to find and choose suitable DLs for DD. When deciding which DLs to use, it's important to think about things like distribution feeder capabilities, bus voltage caps, etc.

With an ever-increasing frequency, utilities are incorporating microgrids into their LV distribution networks. There will be several difficulties encountered by an islanded microgrid during operation due to the growing number of DG (Distributed generation). The suggested approach may help solve the problems associated with integrating renewable energy sources with microgrid control. The planned research will examine the effects of various controllers on the microgrid's voltage in an effort to optimize its performance. Controller performance will also be studied with the use of MATLAB/Simulink Sim Power Systems. Protection zone and operating speed of LV microgrid provide significant difficulties during microgrid operation. The suggested approach allows for the right design of a converter that meets future-ride-through (FRT) standards, hence overcoming these difficulties. In order to quantify the value of opinion mining systems and provide context for how they have evolved practically, an assessment metric is used. Included are criteria that fall below the industry standard of secretive assessment techniques. Recall, Precision, and Accuracy are some of the metrics used for this purpose. Calculating the values of these metrics is necessary for using the suggested approach to efficiently classify reviews using mining.

The performance analysis with the evaluation metrics Precision and F-Measure are given in Figure 2.

The measure of accuracy is given in Figure 3 along with the pictorial representation. In conjunction with the product reviews, the performance was analysed. These reviews were evaluated based on their precision, recall, accuracy, and F-measure scores. This demonstrates how useful opinion and classification methods may be when using sentiment analysis.

The investigation of literature review carried out for the paper addressed the above issues, and used DD as a means to absorb the variations in RE generation. A framework similar to DR is proposed for DD and is evaluated considering numerous Demand Side Response (DSR) aggregators. DSR aggregators help utilities in the identification and control of DLs. On a distribution network RE is manifested mostly as DG and there is scope for power balance management when the DG capacity is within the limits of distribution network. To address the issues on a distribution network, laboratory emulator based study is helpful besides modules necessary for deploying DD have been designed and developed as a part of the investigation. It is found that DD is effective in addressing the issues related to high wind power injection in a large utility grid. The investigation carried out used the concept of Internet of Things (IoT) for data exchange and controlling DLs for efficient DD operation.

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PhD Scholar, Kalinga University, Raipur (CG)