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Integration of PV resources in distribution networks has become a world-wide trend. This has been necessitated by the ever-decreasing photovoltaic (PV) technology cost and the need for clean, carbon-emission-free power generation. Furthermore, the current situation which has resulted in high oil and gas prices has paved extra way for more integration of renewable energy (REs) technologies such as solar PV and wind. However, high proliferation of PV power has several negative effects for the grid. For example, there is a high likelihood of over-voltage occurrences in the network, potential reverse power flow (which can affect protection system operation) and line congestion which may lead to thermal capitulation of conductors and cables. It is, therefore, important to establish the limit of PV that can be injected in the network without stretching the system operating performance indicators into extreme violation, here-in called the PV hosting capacity (PVHC). PV hosting capacity is estimated either by deterministic means or by stochastic methods. Deterministic methods are very good at obtaining the siting and optimal sizing of mega PV plants. The weakness of deterministic methods is that they do not incorporate uncertainty in the load demand nor the uncertainty in the PV output. Thus, their models are mostly unrealistic. The stochastic methods are very good at incorporating uncertainties and model the system input random variables in a more realistic way. However, stochastic methods also suffer from unrealistically too many scenarios to be considered for an accurate analysis. This results in a huge computational burden and large memory requirement. Furthermore, in most of these estimations, the voltage limit is set as a hard constraint for estimating the PVHC. In this paper, we propose a 2-stage method employing both the deterministic and the stochastic approaches. The deterministic stage is used to obtain the optimum PV plant locations and the optimum sizing ratios. The stochastic stage is used for incorporating uncertainty in the input random variables. We also propose the use of probabilistic voltage violation framework to explore extra PV installation for slight voltage violation tolerant feeders or networks. This is then used to obtain output probabilistic maximum node voltages for different PV sizes, which are used to estimate the PV hosting capacity of a network. The efficacy and validity of the approach is demonstrated through numerical simulations conducted on IEEE test distribution networks.

The introduction of a large number of photovoltaic systems to distribution systems has increased the complexity of the voltage profile. It is thus necessary to manage voltage at a higher system level. For this purpose, the voltage profile of the distribution system can be calculated using state estimation and the voltage can be controlled based on the estimated values. Many methods for estimating voltage profiles using state estimation have been developed. However, the estimation accuracy of voltage profiles required for proper voltage control has not been discussed. If the assumed required estimation accuracy is too high, then more meters than necessary will be installed, unnecessarily increasing costs. Conversely, if the assumed required estimation accuracy is too low, voltage control equipment will not operate properly, and voltage violations may occur. Thus, it is important to determine the required estimation accuracy of voltage profiles for proper voltage control. In this paper, the estimation error of the voltage profile is expressed using a probability density function, and the estimation accuracy of voltage profiles required for proper voltage control is examined. A numerical case study was performed on a distribution network model with 2160 consumers, and revealed the following. To prevent voltage violations, an estimation accuracy of voltage profile that is approximately 10 times lower than that of the estimation accuracy based on current measurement equipment data is sufficient. To minimize the number of tap operations, an estimation accuracy needs to be improved by 70% compared with the estimation accuracy based on current measurement equipment data. [ABSTRACT FROM AUTHOR]

With many distribution networks adopting photovoltaic (PV) generation systems in their networks, there is a significant risk of over-voltages, reverse power flow, line congestion, and increased harmonics. Therefore, there is a need to estimate the amount of PV that can be injected into the distribution network without pushing the network towards these threats. The largest amount of PV a distribution system can accommodate is the PV hosting capacity (PVHC). The paper proposes an efficient method for estimating the PVHC of distribution networks that combines particle swarm optimization (PSO) and the gradient descent algorithm (GD). PSO has a powerful exploration of the solution space but poor exploitation of the local search. On the other hand, GD has great exploitation of local search to obtain local optima but needs better global search capabilities. The proposed method aims to harness the advantages of both PSO and GD while alleviating the ills of each. The numerical case studies show that the proposed method is more efficient, stable, and superior to the other meta-heuristic approaches. [ABSTRACT FROM AUTHOR]

In order that the supply of electric power remain stable and sustainable, a certain level of power quality must be maintained while securing energy resources and resolving environmental issues. The distributed generation (DG) has become an essential and indispensable element of such distribution systems from both an environmental and an energy security perspective. However, the installation of DGs in distribution systems may cause variations of voltage on distribution feeder because the DG power outputs may change randomly and dramatically. Generally, the sending voltage at substation in distribution systems can be regulated by an under load tap changer (ULTC) designed to maintain customers' receiving voltages within a predetermined range. Voltage regulation performed by ULTC calls for two parameters: the equivalent impedance and the load center voltage. Therefore, in distribution networks with DG interconnections, these parameters should be designed with attention not only to load variations but also to DG outputs. This paper proposes a method for designing ULTC parameters with reference to variations in DG output. The proposed algorithm is tested with IEEE 13-node system and Korean model network. The results demonstrate the validity of the proposed design method for varying DG distribution. Index Terms--Distributed generation, LDC parameters (equivalent impedance, load center voltage), line drop compensation (LDC), under load tap changer (ULTC), voltage regulation.

In this paper, the authors propose a fuzzy-optimization approach to dynamic economic dispatch, considering the uncertainties in deregulated energy and reserve markets. The methodology was developed from the viewpoint of a generation company wishing to maximize its own profit and to hedge its risks as a participant in the energy market and 10-min spinning reserve market. The uncertainties in the current paper were represented with fuzzy numbers, and consist of the demand and reserves required in each market, prices cleared in each market, and the probability that reserves are called upon in actual operation. The energy and reserve markets were coordinated by the generator ramp rate limits. The optimal amounts of power and reserve were determined by solving the optimization problem. Two reserve payment methods, payment for power delivered and payment for reserve allocated, were investigated in this paper. Index Terms--Competitive environment, deregulation, dynamic economic dispatch, fuzzy approach, restructured power system.

With the opening of the power industry to competition, the power system structure is changing. According to these changes, power system operation, planning, and control need modifications. In the past, utilities had to produce power to satisfy their customers with objective to minimize costs and all demand/reserve were met. However, it is not necessary in a restructured system. Under new structure, generation companies (GENCOs) schedule their generators with objective to maximize their own profit without regard for system social benefit. Power and reserve prices become important factors in decision process. This paper proposes a new tool, profit-based unit commitment (UC) considering power and reserve generatings. The proposed method helps GENCO to make a decision, how much power and reserve should be sold in markets, and how to schedule generators in order to receive the maximum profit. A hybrid method between Lagrange Relaxation (LR) and evolutionary programming (EP) is used for solving this problem. Simulations are carried out to show the performance of the proposed methodology. Index Terms--Competitive environment, deregulation, evolutionary programming, Lagrange relaxation, power system operation planning and control, profit-base unit commitment, restructured system.

Dynamic economic dispatch (DED) is one of the main functions of power generation operation and control. It determines the optimal settings of generator units with predicted load demand over a certain period of time. The objective is to operate an electric power system most economically while the system is operating within its security limits. This paper proposes a new hybrid methodology for solving DED. The proposed method is developed in such a way that a simple evolutionary programming (EP) is applied as a based level search, which can give a good direction to the optimal global region, and a local search sequential quadratic programming (SQP) is used as a fine tuning to determine the optimal solution at the final. A ten-unit test system with nonsmooth fuel cost function is used to illustrate the effectiveness of the proposed method compared with those obtained from EP and SQP alone. Index Terms--Dynamic economic dispatch (DED), evolutionary programming (EP), global solution, power generation operation and control, sequential quadratic programming (SQP), stochastic optimization techniques.

A new dual-stage actuator for positioning a magnetic head for high-density hard disk drives was proposed and developed. This dual-stage actuator device is characterized by the sputter-deposited thin-film PZT, featuring large displacement of 1.2 [micro]m and high-resonance frequency over 15 kHz. In this paper, reliability against temperature, humidity, and mechanical shock was investigated to ensure the practical application to the drive products. Index Terms--Dual-stage actuator, magnetic disk drive, microactuator, piezoelectric actuator, PZT.