Distributed Generation Integration Based on Optimization Techniques for Cost Minimization and Improve Networks Characteristics

The power grid has gradually changed its operation during the recent decades. These developments have encouraged a shift from centralized to decentralized power flow control. A research has been carried out to investigate the possibilities to control power flows using the Smart Power Router (SPR) in distribution networks, where a large share of distributed generation (DG) is present. In this paper, the possible applications of the SPR have been explored in a laboratory test network. In particular, the SPR's control capabilities for both active and reactive power flows were enhanced to improve the system performance. An overview of the control of the SPR is presented. The effect of the power flow management with reconfigurations in the distribution network is considered. Furthermore, the possibilities for decoupled control of the active and the reactive power flow are elaborated.

With increasing connection of distributed generations (DGs), the power flow in electric distribution network is no longer unidirectional and the network has become active distribution network. Thus, the connection of DGs in electric distribution networks has created a challenge for distribution network operators due to bidirectional power flow. One of the main technical challenges of an active distribution network is to maintain an acceptable voltage level in the system. This has initiated efforts in using various voltage control strategies to regulate the network voltage profile so that the voltage is maintained at its allowable voltage limits. A number of voltage control methods have been applied to solve voltage control problems associated with the connection of DGs in a distribution system. These voltage control strategies may be classified as decentralized or distributed and centralized or coordinated voltage control. From the literature, a number of coordinated voltage control strategies have been implemented to provide better and faster control to the system. This chapter presents an improved coordinated voltage control method in active electric distribution network by coordinating the three voltage control methods, namely, power factor control, on load tap changer control and generation curtailment control. These voltage control methods are coordinated using fuzzy logic by considering the load bus voltages and DG power as inputs and the voltage control actions as the outputs. The fuzzy logic if-then control rules which are generated to build the fuzzy logic control system are based on the simulations results as well as from the previous works. Results obtained using the fuzzy logic based coordinated voltage control has shown that the voltages are able to be kept within its permissible limits.

With rapid increase in wind power penetration into the power grid, unbalanced power flow in distribution network with distributed generators is becoming increasingly important to power sysytem. Due to the variability and uncertainty of wind, such traditional unbalanced power flow analysis method for distributed power supply limit the use of the existing tools for decision-making under uncertain conditions. As a result, unbalanced power flow analysis method, which provides information on uncertainty associated with wind power analysis, is gaining increased attention. This paper presents a novel hybrid intelligent algorithm for deterministic unbalanced power flow in distribution network with distributed generators that utilizes a combination of Cauchy particle swarm optimization(CPSO) and fuzzy ARTMAP (FA) network, which is optimized by using PSD-BPA. The performance of the proposed model is assessed utilizing wind power data from the Baolihua wind farm in Southern China. Unbalanced power flow in distribution network with distributed generators is researched. The typical static load model is established and the impact of distributed generators on feeder voltage profile is analyzed under unbalanced loads. The simulation results are given. References for the global planning of unbalance compensation in distribution network can be provided by the study.

Power losses and voltage deviations in distribution power networks (DPNs) are high since they carry more power demand than transmission power networks. Also, voltage deviation beyond the allowable range causes voltage stability problems in the DPN. The power loss (PL) in the DPN should be kept at the minimum level for the economic operation of the electric grid. Integrating distributed generation (DG) in appropriate sites of the power networks can minimize the power losses and voltage drops. An integrated optimization approach is proposed in this paper, by combining an analytical and metaheuristic algorithm to optimize the placement and sizing of multiple DGs. The active power loss sensitivity (APLS) index is an analytical mathematical computation approach used to identify the optimal bus locations for DG placement. The modified ant lion optimization (MALO) algorithm is applied to optimize the ratings of the DG systems. The MALO algorithm is proposed by adopting the Lévy flights (LF) pattern in the random walk process (RWP). LF representation of RWPs enhances the exploration phase of the ALO algorithm and helps to obtain the near-optimal solution. The proposed integrated approach optimizes multiple units of photovoltaic (PV) and wind turbine (WT) units to minimize the multi-objective function, including AP loss and voltage deviation (VD) minimizations. The effectiveness of the proposed integrated approach is validated on the IEEE 69-bus, 85-bus, and 118-bus radial DPNs. Besides, the simulation study is extended for ant lion optimization (ALO), BAT, and artificial bee colony (ABC) algorithms-based techniques. The integrated approach has reduced the total AP loss of the IEEE 69-bus and 85-bus radial DPN from 225 kW to 70.51 kW and 316.12 kW to 162.80 kW, respectively, for the optimized three PV DG units allocation. Likewise, the total AP loss of the 118-bus radial DPN is cut down from 1296.3 kW to 432.3 kW after the optimized five PV DG units allocation. Meanwhile, the total AP LOSS of the 69-bus, 85-bus, and 118-bus radial DPNs is reduced to 4.78 kW, 53.87 kW, and 112.2 kW, respectively, after the optimized WT DG allocation. Additionally, the optimized inclusion of multiple DG units significantly minimized the VD of the DPNs. The minimum VD of the 69-bus, 85-bus, and 118-bus test systems is reduced from 0.0908 p.u., 0.1297 p.u., and 0.1312 p.u. to 0.0174 p.u., 0.0384 p.u., and 0.0201 p.u., respectively, for the multiple PV unit allocations. Similarly, the minimum VDs of the 69-bus, 85-bus, and 118-bus radial DPNs are minimized to 0.0048 p.u., 0.0190 p.u., and 0.0093 p.u., respectively, following the multiple WT DG unit allocations. The simulation findings of the APLS-MALO integrated approach are related to the various optimization techniques. The comparative study reveals that the proposed integrated approach gives a more effective and efficient solution than ALO, BAT, ABC, and other optimization techniques. Finally, the simulation findings of the APLS-MALO integrated technique are verified via the calculation of conventional statistical metrics and the conduction of a non-parametric Wilcoxon test.

While penetration of Distributed Generators (DGs) has been increasing with favorable government policies coupled with technological advances in portable deployment of DGs, the challenges of efficient network operations in secondary distribution networks have also become formidable. Among other issues, the real power penetration from PV and Wind generators causes imbalance between real and reactive power flows in low voltage distribution networks where phase unbalancing and variation in demand-supply have dominant effect on voltage profile, overloads, power factor and losses. This paper attempts to apply sequential time simulations approach by performing distribution power flow simulations for time-series data points of demand and generation over a period of one year at 15 minute intervals. This approach helps in capturing the diurnal and seasonal variability of intermittent power producing DGs and that of load profiles. It also helps in undertaking time integral studies for energy losses, number of switching operations by control devices such as shunt compensators and voltage regulators. The results of this exercise are used to propose different switching strategies to limit voltage limit violations while reducing annual energy losses.

The power industry has undergone significant recent changes due to the growing demand for a cleaner and more sustainable energy mix. In this context, the Brazilian government began encouraging distributed generation (DG), making photovoltaic generation a strong trend in the country. However, the expansion of DG may cause negative impacts on the grid, such as reverse power flow (RPF) and overvoltages, which motivates research aimed at mitigating these effects. This study proposes strategies to mitigate RPF in distribution networks with high penetration of photovoltaic generation and evaluates their impacts on the electrical system. Three strategies were analyzed: a battery energy storage system (BESS), a control mechanism using a Grid Zero inverter, and PV Curtailment. The strategies were implemented in OpenDSS on a real distribution network located in São Paulo, Brazil. The assessment involved analyzing power flow in critical transformers and at the substation, as well as monitoring bus voltages and network energy losses. The quantitative results demonstrate that BESS allocation was the superior strategy, reducing technical losses by 61.3% and fully mitigating reverse power flow under steady-state conditions. The Grid Zero inverter eliminated power injection into the grid; however, it increased substation dependency by 59% compared to the baseline scenario. PV curtailment, although achieving the smallest reduction in RPF, proved to be the most effective technique for power quality, limiting the average voltage rise to 0.7%.

Participation of adjustable loads in demand response can alleviate power supply and demand challenges. As a typical adjustable load, this paper gives an optimal scheduling method considering optimal power flow in distribution networks of air source heat pumps, and studies adjustable load power allocation schemes for multiple distribution network nodes. The scheme with the goal of the lowest network loss determines the scheduling amount of the corresponding node heat supply stations to achieve optimal power flow dispatch, and the genetic algorithm is used to solve the model. Specific examples analysis shows that the proposed decision-making method can reasonably allocate power adjustments, prevent node voltage overruns in distribution networks, and reduce active power losses.

Power flow in distribution networks with earth return

Multi-faceted sustainability improvement in low voltage power distribution network employing DG and capacitor bank

A per-node granularity decentralized optimal power flow for radial distribution networks with PV and EV integration

Optimal power flow (OPF) is a crucial aspect of distribution network planning and operation. Conventional heuristic algorithms fail to meet the system requirements for speed and accuracy, while linearized OPF approaches are inadequate for distribution networks with high R/X ratios. To address these issues and cater to multi-period scenarios, this study proposes a dynamic linearized second-order cone programming-based (SOCP) OPF model. The model is built by first establishing a dynamic OPF model based on linearized second-order conic relaxation (LSOCR-DOPF). The components of the active distribution network, such as renewable energy power generation units, energy storage units, on-load-tap-changers, static var compensators, and capacitor banks, are then separately modeled. The model is implemented in MATLAB and solved by YALMIP and GUROBI. Finally, three representative scenarios are used to evaluate the model accuracy and effectiveness. The results show that the proposed LSOCR-DOPF model can ensure calculation time within 3 min, voltage stability, and error control within 10−6 for all three applications. This method has strong practical value in the fields of active distribution network day-ahead dispatch, accurate modeling of ZIP load, and real-time operation.

Day-ahead multi-period scheduling can provide reasonable operation plans for various power sources and energy storage systems in distribution networks, so as to reduce costs and improve energy utilization. In this paper, the dynamic power flow optimization model of radial distribution network with wind/PV/energy storage is established firstly. The model takes the output of various power sources and the charge-discharge power of energy storage systems as decision variables, which belongs to nonconvex nonlinear programming problems. Then the second-order cone relaxation is applied to the power flow equation, and the problem is transformed into a second-order cone programming model which can be solved strictly and efficiently. Finally, the extended IEEE 30-node system is simulated, and the software Gurobi is used to obtain the optimal scheduling scheme, which verifies the effectiveness of this method. Test results show that the operating cost is reduced by adding wind/PV/energy storage into networks, and the consumption of renewable energy is increased by considering the punishment for wind and PV abandonment.

The world is transitioning to utilise distributed generation (DG) to reduce social, economic and environmental effects. As DG penetration increases, determining the associated impacts on distribution networks is vital. This paper proposes a concept around sphere of influence of single and multiple DG units through line current analysis and integrated Sankey and Geospatial visualisation approaches. With the increasing integration of renewable energy sources and the need for grid stability, it becomes imperative to analyse the sphere of influence of DGs in geographic context. The main contribution of the paper is to present the visualisation techniques to determine the sphere of influence of DGs within power networks by considering single and multiple DGs. The proposed study combines the power of Sankey visualisation, a flow diagram technique, with geospatial data to gain insights into the DG sphere. The advantage of the proposed method is it acts as a visual lens to determine DG clusters, flow of power, coherency analysis, voltage support, resource allocation, operational planning, and grid resilience assessment. The proposed method is tested using 33 bus distribution network and visualisation on a representative geospatial map is carried out using Python library and Tableau.

For a long time, the identification of 10kV distribution network line loss anomalies has been a very difficult task, with the large number of distributed photovoltaics (DPV) access, the distribution network power flow from one-way flow to two-way flow, which greatly affects the line power flow, voltage and network loss, and increases the difficulty of identifying abnormal distribution network line loss. Therefore, a method for identifying line loss anomalies in distribution network considering distributed photovoltaic access is proposed. Firstly, the characteristic index of distribution network line loss after distributed photovoltaic access is extracted, and the correlation between the line loss index and line loss of the distribution network is calculated by using the gray correlation analysis method, and the correlation degree is ranked. Then, according to the order of gray correlation degree, appropriate indicators are selected for fuzzy C-means clustering, and outliers in each category are detected based on the K-nearest neighbor algorithm to determine whether there is an abnormal line loss in the distribution network. Finally, the verification analysis is carried out through the example, and the results show that the proposed method can effectively identify whether abnormal line loss occurs in the distribution network containing distributed photovoltaics.

The optimal power flow (OPF) problem seeks to control the power generation/consumption to minimize the generation cost, and is becoming important for distribution networks. OPF is nonconvex and a second-order cone programming (SOCP) relaxation has been proposed to solve it. We prove that after a small modification to OPF, the SOCP relaxation is exact under a mild condition. Empirical studies demonstrate that the modification to OPF is small and that the mild condition holds for all test networks, including the IEEE 13-bus test network and practical networks with high penetration of distributed generation.