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In the past extensive researches have been conducted on demand side management (DSM) program which aims at reducing peak loads and saving electricity cost. In this paper, we propose a framework to study decentralized household demand side management in a residential distribution network which consists of multiple smart homes with schedulable electrical appliances and some rooftop photovoltaic generation units. Each smart home makes individual appliance scheduling to optimize the electric energy cost according to the day-ahead forecast of electricity prices and its willingness for convenience sacrifice. Using the developed simulation model, we examine the performance of decentralized household DSM and study their impacts on the distribution network operation and renewable integration, in terms of utilization efficiency of rooftop PV generation, overall voltage deviation, real power loss, and possible reverse power flows., Comment: 5 pages, 7 figures, ISGT 2018 conference

Nowadays common practice in deploying photovoltaic distributed generations (PVDGs) is customer-based installation in the distribution network. Increasing level of PVDG applications and expedite approval by utilities have raised concern about the negative impacts of PVDG installations on the distribution network operations such as reverse power flows and undesirable voltage fluctuations. One potential solutions is to optimize the siting and sizing of these distributed renewable generation resources. This paper presents a comparative study on both optimal and randomized installation of PVDGs with the latter modeling real life customer-based renewable integration. The proposed models examine and compare the impacts of PVDG installation on distribution network operation. Numerical simulations have been performed on a local distribution network model with realistic load profiles, GIS information, local solar insolation, and feeder and voltage settings. It is found that when the distribution system has a medium penetration ratio optimal PVDG installations may introduce essential improvements in terms of voltage deviation and energy loss reduction than randomized installation. However, if the penetration ratio is very low or extremely high there will be not significant difference between the two., Comment: 5 pages, 7 figures, conference

Transformers and transmission lines are critical components of a grid network. This paper analyzes the statistical properties of the electrical parameters of transmission branches and especially examines their interdependence on the voltage levels. Some interesting findings include: (a) with appropriate conversion of MVA rating, a transformers per unit reactance exhibits consistent statistical pattern independent of voltage levels and capacity; (b) the distributed reactance (ohms/km) of transmission lines also has some consistent patterns regardless of voltage levels; (c) other parameters such as the branch resistance, the MVA ratings, the transmission line length, etc, manifest strong interdependence on the voltage levels which can be approximated by a power function with different power constants. The results will be useful in both creation of synthetic power grid test cases and validation of existing grid models., Comment: To be published (Accepted) in: Proceedings of the 51st Hawaii International Conference on System Sciences (HICSS), Big Island, HI, USA, 2018

This paper investigates the problem of generation and load settings in a synthetic power grid modeling of high-voltage transmission network, considering both electrical parameters and topology measures. Our previous study indicated that the relative location of generation and load buses in a realistic grid are not random but correlated. And an entropy based optimization approach has been proposed to determine a set of correlated siting for generation and load buses in a synthetic grid modeling. Using the exponential distribution of individual generation capacity or load settings in a grid, and the non-trivial correlation between the generation capacity or load setting and the nodal degree of a generation or load bus we develop an approach to generate a statistically correct random set of generation capacities and load settings, and then assign them to each generation or load bus in a grid.

Vehicle-to-grid (V2G) technology has drawn great interest in the recent years and its efficiency depends on scheduling of charging process of plug-in electric vehicles (PEVs) as small portable power plants in smart parking lots. On the other hand, active shift from centralized to decentralized power generation and environmental concerns have caused an increase in the utilization of combined heat and power (CHP) units in power systems. The goal of this study is to develop and simulate a novel approach for combined heat and power unit commitment with PEVs for cost reduction in electric power system. A schedule for charging and discharging processes of PEVs with respect to load curve variations is proposed in this paper. A modified test system consisting of conventional TG (thermal power generating) units, CHP units, and PEVs is employed to investigate the impacts of PEVs on generation scheduling. For the modified test system, results obtained are encouraging and indicate both the feasibility of the proposed technique and its effectiveness on generation scheduling.

Demand Side Management (DSM) strategies are of-ten associated with the objectives of smoothing the load curve and reducing peak load. Although the future of demand side manage-ment is technically dependent on remote and automatic control of residential loads, the end-users play a significant role by shifting the use of appliances to the off-peak hours when they are exposed to Day-ahead market price. This paper proposes an optimum so-lution to the problem of scheduling of household demand side management in the presence of PV generation under a set of tech-nical constraints such as dynamic electricity pricing and voltage deviation. The proposed solution is implemented based on the Clonal Selection Algorithm (CSA). This solution is evaluated through a set of scenarios and simulation results show that the proposed approach results in the reduction of electricity bills and the import of energy from the grid.

The optimization of the size and location of Photovoltaic (PV) Distributed Generation (DG) is a method for reducing distribution networks loss, cutting costs for utilities, and integrating renewable energy into the power grid. However, this optimization problem is a difficult mixed continuous discrete problem that is difficult to solve efficiently. Herein, we propose a novel Sequential PVDG Placement algorithm which utilizes a Downstream Power Index (DPI) for PV allocation. We compare Sequential PVDG Placement results with those of Shuffled Frog Leaping Algorithm (SFLA), a memetic heuristic algorithm. Our algorithm demonstrates similar accuracy and drastically less computation time compared to SFLA. Given its high accuracy and low computation time, sequential PVDG allocation algorithm may be useful for rapid online power dispatch, long-term planning, and microgrid operations under high penetration of renewable sources., Comment: To be published (Accepted) in: Proceedings of the 49th North American Power Symposium (NAPS), Morgantown, WV, USA, 2017

Remarkable penetration of renewable energy in electric networks, despite its valuable opportunities, such as power loss reduction and loadability improvements, has raised concerns for system operators. Such huge penetration can lead to a violation of the grid requirements, such as voltage and current limits and reverse power flow. Optimal placement and sizing of Distributed Generation (DG) are one of the best ways to strengthen the efficiency of the power systems. This paper builds a simulation model for the local distribution network based on obtained load profiles, GIS information, solar insolation, feeder and voltage settings, and define the optimization problem of solar PVDG installation to determine the optimal siting and sizing for different penetration levels with different objective functions. The objective functions include voltage profile improvement and energy loss minimization and the considered constraints include the physical distribution network constraints (AC power flow), the PV capacity constraint, and the voltage and reverse power flow constraints., Comment: To be published (Accepted) in: Proceedings of the IEEE PES Innovative Smart Grid Technologies Conference (ISGT), Washington D.C., USA, 2017

Increasing penetration level of photovoltaic (PV) distributed generation (DG) into distribution networks will have many impacts on nominal circuit operating conditions including voltage quality and reverse power flow issues. In U.S. most studies on PVDG impacts on distribution networks are performed for west coast and central states. The objective of this paper is to study the impacts of PVDG integration on local distribution network based on real-world settings for network parameters and time-series analysis. PVDG penetration level is considered to find the hosting capacity of the network without having major issues in terms of voltage quality and reverse power flow. Time-series analyses show that distributed installation of PVDGs on commercial buses has the maximum network energy loss reduction and larger penetration ratios for them. Additionally, the penetration ratio thresholds for which there will be no power quality and reverse power flow issues and optimal allocation of PVDG and penetration levels are identified for different installation scenarios., Comment: To be published (Accepted) in: 12th IEEE PES PowerTech Conference, Manchester, UK, 2017

This paper presents a set of validation metrics for transmission network parameters that is applicable in both creation of synthetic power system test cases and validation of existing models. Using actual data from two real-world power grids, statistical analyses are performed to extract some useful statistics on transformers and transmission lines electrical parameters including per unit reactance, MVA rating, and their X/R ratio. It is found that conversion of per unit reactance calculated on system common base to transformer own power base will significantly stabilize its range and remove the correlation between per unit X and MVA rating. This is fairly consistent for transformers with different voltage levels and sizes and can be utilized as a strong validation metric for synthetic models. It is found that transmission lines exhibit different statistical properties than transformers with different distribution and range for the parameters. In addition, statistical analysis shows that the empirical PDF of transmission network electrical parameters can be approximated with mathematical distribution functions which would help appropriately characterize them in synthetic power networks. Kullback-Leibler divergence is used as a measure for goodness of fit for approximated distributions., Comment: To be published (Accepted) in: The 10th Bulk Power Systems Dynamics and Control Symposium (IREP2017), Espinho, Portugal, 2017

While power systems are reliable infrastructures, their complex interconnectivities allow for propagation of disturbances through cascading failures which causes blackouts. Meanwhile the ever increasing penetration level of renewable generation into power grids introduces a massive amount of uncertainty to the grid that might have a severe impact on grid vulnerability to overload cascading failures. There are numerous studies in the literature that focus on modeling cascading failures with different approaches. However, there is a need for studies that simulate cascading failure considering the uncertainty coming from high penetration of renewable generation. In this study, the impacts of wind generation in terms of its penetration and uncertainty levels on grid vulnerability to cascading overload failures are studied. The simulation results on IEEE 300 bus system show that uncertainty coming from wind energy have severe impact on grid vulnerability to cascading overload failures. Results also suggest that higher penetration levels of wind energy if not managed appropriately will add to this severity due to injection of higher uncertainties into the grid., Comment: To be published (Accepted) in: Proceedings of the Power and Energy Society General Meeting (PESGM), Chicago, IL, 2017

In this paper we study the problem of optimal layout of an offshore wind farm to minimize the wake effect impacts. Considering the specific requirements of concerned offshore wind farm, we propose an adaptive genetic algorithm (AGA) which introduces location swaps to replace random crossovers in conventional GAs. That way the total number of turbines in the resulting layout will be effectively kept to the initially specified value. We experiment the proposed AGA method on three cases with free wind speed of 12 m/s, 20 m/s, and a typical offshore wind distribution setting respectively. Numerical results verify the effectiveness of our proposed algorithm which achieves a much faster convergence compared to conventional GA algorithms.