شبکه تجزیه و تحلیل سیستم توزیع در حضور تولید پراکنده با زمان های مختلف مدل بار

With the implementation of competitive electricity supply system all over the world, there is the need of optimal utilization of the existing generation sources at the same time to curtail the generation from the conventional power plants and add the renewable sources generation looking into the environmental concern and limitation of the fossil fuels. This has lead to the motivation for the studies on the integration of distributed resources to the grid. In this paper optimal locations and sizes for DG is determined for weakly meshed distribution networks based on the sensitivity method. Novel method based on loss sensitivity is used in this paper to determine optimal size and location of DGs. The modified Novel method is proposed for DG allocation. The main contribution of the paper is: (i) distributed generation allocation for mesh network using sensitivity approach, (ii) modified Novel method for DG allocation and sizes calculation for meshed distribution system with load variations, (iii) comparison of the results obtained with single and two DG placement with load variations, (iv) the loss savings and overall cost savings per annum with single and two DGs placement with load variations. In this paper we considered the impact of time varying load flow with realistic load model. The realistic ZIP load model has been considered for study. The results have been obtained for a distribution network of UK Distribution Corporation consisting of 38 buses. The results have also been obtained for radial distribution system for comparison.

With technological improvements micro-turbines, fuel cells, mini-hydro, battery storage etc., this has provided an opportunity for large-scale integration of these generation sources called as distributed energy sources (DERs) into distribution systems. These distributed generation sources are modular in design and can be deployed near load sites to address increasing power demand of the current electric utilities [1], [2], [3], [4], [5] and [6]. The integration of the DGs may provide technical as well as economical benefits by supplying loads during peak load periods, when the cost of electricity is higher. DG can best serve as a price hedging mechanism in real time pricing mechanism in the new competitive electricity market regime. However, penetration and viability of DG at a particular location is influenced by technical as well as economic factors. The technical merits of DG integration include voltage support, energy-loss reduction, release of system capacity, and improve utility system reliability [1], [4], [5] and [6]. Economical merit, on the other hand, encompasses hedge against high electricity price. The distributed generation renewable resources such as; small hydro, wind, solar energy can be integrated into distribution systems with the several issues related to technical barriers that are challenging. Distributed Generation is electricity production that is on-site or close to the load center to avoid the need of the network expansions in order to cover new load centers and to support the increased energy transfer which would be necessary for satisfying consumers increased demand. DG can be an alternative for residential, commercial, and industrial applications. However, distributed generation can be defined in a variety of ways as reported in the literature [1], [2], [3] and [4]. The impact of DG on radial distribution network is explained i.e., voltage support, loss reduction, and distribution capacity release and power quality issues in [5]. There are many reasons behind the increasingly widespread use of DG deferring the Transmission and Distribution (T&D) costs, good efficiencies especially in cogeneration and in combined cycles, are reduced, creating opportunities for new utilities in the power generation sector, provides a flexible way to choose a wide range of combinations of cost and reliability [6]. DG impacts different parameters of a power system, comprising voltage profile, line losses, and short circuit current, amount of injected harmonic, and system reliability and stability and the installation of DG units should be allocated in an optimal way to maximize the system efficiency. To analyze the distributed energy resources (DER) impacts, different types of ‘generator groups’ can be considered [6]. In [7] a new method has been suggested based on nodal pricing for optimally allocating DG in radial distribution system. Authors in [8] presented a method for optimal sitting and sizing of multiple distributed generators (DGs) using particle swarm optimization (PSO) based approach. A simple and effective cumulative performance index, utilizing voltage profile improvement, loss reduction, and voltage stability index improvement is considered. Loss sensitivity factor, based on equivalent current injection method for sizing and sitting of DG in radial distribution system is given in [9]. Calculation of cost of DG is given in [10] based on conventional, triangular, and complex power limit. Authors in [11] described a technique for selection of buses in a sub transmission system for location of distributed generation (DG) and determination of their optimum capacities by minimizing transmission losses. The buses have been selected based on incremental voltage (dV/dP) sensitivities. Ref. [12] presented two new methodologies for optimal placement of distributed generation sources using an optimal power flow (OPF) based model in real time wholesale electricity market. The problem of optimal placement, including size, is formulated for two different objectives, namely, social welfare maximization and profit maximization. The candidate locations for DG placement are identified on the basis of locational marginal price (LMP). Optimal sizing and sitting decisions for DG capacity planning using heuristic approach was proposed in [13]. A multi-objective optimization approach using evolutionary algorithm with an objective of minimizing cost of energy losses, network upgrading and service interruptions for sizing and sitting of DG in distribution systems has been presented in [14]. An analytical expression based on real power loss sensitivity to calculate optimal DG size and optimal location of DG minimizing power losses in a distribution network was proposed in [15]. A new methodology given in [16] using Fuzzy and Artificial Immune System (AIS) for the placement of Distributed Generators (DGs) in a radial distribution system to reduce the real power losses and to improve the voltage profile. In the first stage, the Fuzzy Set approach is used to find the optimal DG locations and in the second stage, Clonal Selection algorithm of AIS is used to size the DGs corresponding to maximum loss reduction. This algorithm is a new, population based, optimization method inspired by the cloning principle of the human body immune system. Paper [17] deals with impact of voltage dependent load models on the predicted energy losses in DG planning. A multi-objective optimization approach considering losses reduction and voltage profile improvement for DG allocation using GA was proposed in [18]. In [19] describes a novel methodology to calculate optimal DG sizes based on real power loss. An analytic method which can be used to determine the optimal placement and sizing of DG without use of admittance, impedance or Jacobian matrix with only one power flow for radial systems is presented in [20]. Ref. [21] presented an approach to determine the optimal sitting and sizing of DG with multi system constraints to achieve a single or multi-objectives using genetic algorithm (GA). It deals with the benefits (voltage profile improvement, spinning reserve increasing, power flow reduction and total line loss reduction) obtained with optimal DG installation. Authors in [22] presented a simple method for investigating the problem of optimal location and capacity of DG in three-phase unbalanced radial distribution systems (URDS) for power loss minimization and to improve the voltage profile of the system using voltage index (VSI) analysis. Loss sensitivity factors (LSF) are used [23] to select the candidate locations for the multiple DG placements and Simulated Annealing (SA) is used to estimate the optimal size of DGs at the optimal locations. A new method for optimal sizing and sitting of DG in radial distribution systems was proposed in [24]. In this, optimal location for DG obtained by power loss sensitivity and optimal size is given by Harmony Search Algorithm (HSA). In [25] optimal placement of DG is given based on loss sensitivity and voltage stability index. An investigation into the effect of load models on the predicted energy losses in DG planning with time varying load demand is presented in [26]. It describes detailed voltage dependent load model, for DG planning use, which considers three categories of loads: residential, industrial and commercial. A value based DG placement for service quality improvement is proposed in [27]. Borges et al. proposed distributed generation allocation for reliability, losses, and voltage improvements [28]. Kumar and Gao proposed mixed integer programming based approach for distributed generation placement using criteria of nodal price reduction along with losses in pool and hybrid electricity markets [29]. However, the method has been proposed for transmission system network. Based on the literature survey, it is observed that distributed generation allocation has been obtained based on the sensitivity and optimal power flow based approaches for radial distribution system. Most of the authors have concentrated on solving radial distribution networks with time invariant load. This paper has presented an effective approach for determining the optimal capacity and location of DG units in meshed power systems. The objective functions considered in the study were maximization of the system loading margin as well as the DISCO’s profit [30]. The time varying load models impact needs to be addressed for distributed generation allocation for mesh network also. The load flow for the mesh system is based on the concept discussed in [31]. Identification-based adaptive voltage regulation (I-BAVR) is proposed in [32] which uses real-time identification of the thevenin equivalent circuit of the system, giving the X/R ratio to identify the active and reactive power dispatch of the DG unit. A new constrained multi objective Particle Swarm Optimization (PSO) is presented in [33], for Wind Turbine Generation Unit (WTGU) and photovoltaic (PV) array placement approach to minimize power loss and improve voltage stability in radial distribution system. A new two-layer simulation-based optimization (SBO) approach is proposed [34] to determine the optimal allocation and capacity of distributed energy resources (DER) in a power distribution system. In the first layer, a dynamic optimal power flow (DOPF) routine is embedded in a simulation algorithm to calculate the cost and reliability level of the system over one year. In the second layer, a particle swarm optimization (PSO) algorithm uses the outputs of the first layer to optimize the location and capacity of wind turbines, PV panels, and grid-scale batteries, in order to minimize cost while meeting reliability requirements. In ref. [35] authors presented a review of optimal DG planning in the distribution systems. In [36], a hybrid method based on improved particle swarm optimization (IPSO) algorithm and Monte Carlo simulation methodology for optimal distributed generation allocation and sizing in distribution systems is proposed to minimize the costs of active and reactive losses, and to improve the voltage profile and reliability of the distribution systems. Heuristic algorithm based on sensitivity indexes methodology is proposed in [37] for the optimal distributed generation allocation associated with the optimal reconfiguration in radial distribution networks to minimize energy losses. The data of the system which has been considered for the analysis is also provided in [38]. In this paper, locations and sizes for DG is determined for mesh distribution network based on the sensitivity method. The Novel method was proposed for unity power factor only [19], however, the Novel method has been modified at lagging power factor to obtain the location and size of DGs as DGs also supply reactive power also for better voltage profile meeting real power demand. The modified Novel method is proposed for DG allocation considering the loss reduction. The main contribution of the paper is: (i) distributed generation allocation for mesh network using sensitivity approach, (ii) modified Novel method for DG allocation and sizes calculation for mesh distribution system with load variations, (iii) comparison of the results obtained with single and two DG placement with load variations, (iv) the loss savings and overall cost savings with single and two DGs placement In this paper, we considered the impact of time varying load flow with realistic ZIP load model. The results have been obtained for the distribution network of UK Distribution Corporation consisting of 38 buses [17]. The results have also been obtained for radial distribution system for comparison.

In this paper, the sensitivity based method has been applied for DG allocation in the mesh network. In the proposed novel power loss sensitivity method, analytical expressions are derived to determine the optimal DG sizes at unity and lagging power factors. Also the proposed approach is extended for optimal multiple DG placement in distribution system. The analysis has been carried out for practical 38 bus test system with time varying ZIP load model. The results have been obtained for both radial and mesh network. The analysis has been carried out under different scenarios of unity and lagging power factor with time varying ZIP load model. The cost of energy loss, cost of powers obtained from DG and savings due to reduction in the losses has been obtained for both radial and mesh network. Based on the results obtained: 1. It is observed that two DG placement is more effective in loss reduction and the voltage profile obtained is better for lagging power factor than at unity power factor. This is due to the fact that DGs also supply reactive power locally and causes more loss reduction. 2. It is observed that the losses reduce with DGs placement and reduction is higher for two DGs case. 3. It is observed that the voltage profile is better with two DGs at lagging power factor for both radial and mesh distribution system. 4. It is observed that two DG placement gives better results than single DG placement in terms of cost of energy loss and savings per annum savings due to loss reduction.