Abstract
Due to the limited predictability and associated uncertainty of renewable energy resources, renewable-based electricity systems are confronted with instability problems. In such power systems, implementation of Demand Response (DR) programs not only can improve the system stability but also enhances market efficiency and system reliability. By implementing cloud-based engineering systems the utilization of DR will be increased and consequently DR will play a more crucial role in the future. Therefore, DR aggregators can efficiently take part in energy, balancing and ancillary services markets. In this paper, a model has been developed to optimize the behavior of a DR aggregator to simultaneously participate in the mentioned markets. To this end, the DR aggregator optimizes its offering/bidding strategies based on the contracts with its customers. In the proposed model, uncertainties of renewable energy resources and the prices of electricity markets are considered. Numerical studies show the effectiveness of the proposed model.

Abstract
' In this paper, a market-based control scheme is proposed to determine the minimum billing cost of responsive demands with the minimum impact on their satisfaction. For this purpose, the responsive demands are modeled as agents who bid to the energy market. In the model, the financial compensation provided by the market motivates the responsive demands to shift their load to off-peak periods. Since dissatisfaction is caused by the deviation from the reference consumption, the responsive demands' bids are dependent on the level of satisfaction that consumers are willing to have. Numerical results reveal that the billing cost of these customers is meaningfully decreased compared to the uncontrolled approaches. In addition, the results are compared to the centralized aggregation-based approach, in which a demand response aggregation entity directly buys energy on behalf of responsive demands in the market. The results indicate the effectiveness of the proposed decentralized market-based scheme.

Abstract
This paper proposes a mixed-integer nonlinear programming approach to maximize the total expected profit of a price-taker hydro producer operating in a pool-based electricity market. Head dependence, commitment decisions, discharge ramping, startup costs and forbidden zones are all effectively handled in our approach. Market uncertainty is modeled via price scenarios and risk management is suitably addressed using conditional value-at-risk. Appropriate offering strategies to the day-ahead market are developed, consisting of hourly supply functions generated for different risk levels. A realistic cascaded hydro system with seven reservoirs is considered as a case study for analyzing and comparing risk-neutral vs. risk-averse results. Conclusions are duly drawn.

Abstract
This paper presents a control technique for enhancing the stable operation of distributed generation (DG) units during islanding and grid-connected modes. The compensation of instantaneous variations in the reference current components of DG units in ac-side, and dc-link voltage variations in dc-side of interfaced converters, are considered properly in the control loop of DG units, which is the main contribution and novelty of this control technique over other control techniques. By using the proposed control technique, DG units can provide the continuous injection of active power from DG sources to the local loads and/or utility grid. Moreover, by setting appropriate reference current components in the control loop of DG units, reactive power and harmonic current components of loads can be supplied with a fast dynamic response. The performance of the developed control is assessed through simulation results during dynamic and steady-state operating conditions.

Abstract
Irradiance received on the earth's surface is the main factor that affects the output power of solar PV plants, and is chiefly determined by the cloud distribution seen in a ground-based sky image at the corresponding moment in time. It is the foundation for those linear extrapolation-based ultra-short-term solar PV power forecasting approaches to obtain the cloud distribution in future sky images from the accurate calculation of cloud motion displacement vectors (CMDVs) by using historical sky images. Theoretically, the CMDV can be obtained from the coordinate of the peak pulse calculated from a Fourier phase correlation theory (FPCT) method through the frequency domain information of sky images. The peak pulse is significant and unique only when the cloud deformation between two consecutive sky images is slight enough, which is likely possible for a very short time interval (such as 1 min or shorter) with common changes in the speed of cloud. Sometimes, there will be more than one pulse with similar values when the deformation of the clouds between two consecutive sky images is comparatively obvious under fast changing cloud speeds. This would probably lead to significant errors if the CMDVs were still only obtained from the single coordinate of the peak value pulse. However, the deformation estimation of clouds between two images and its influence on FPCT-based CMDV calculations are terrifically complex and difficult because the motion of clouds is complicated to describe and model. Therefore, to improve the accuracy and reliability under these circumstances in a simple manner, an image-phase-shift-invariance (IPSI) based CMDV calculation method using FPCT is proposed for minute time scale solar power forecasting. First, multiple different CMDVs are calculated from the corresponding consecutive images pairs obtained through different synchronous rotation angles compared to the original images by using the FPCT method. Second, the final CMDV is generated from all of the calculated CMDVs through a centroid iteration strategy based on its density and distance distribution. Third, the influence of different rotation angle resolution on the final CMDV is analyzed as a means of parameter estimation. Simulations under various scenarios including both thick and thin clouds conditions indicated that the proposed IPSI-based CMDV calculation method using FPCT is more accurate and reliable than the original FPCT method, optimal flow (OF) method, and particle image yelocimetry (PIV) method.

Abstract
Since the concerns for energy conservation and environment are growing, renewable energy resources are rapidly increasing, which lead to additional variability and uncertainty for the power system operators. The uncertainty imposes considerable technical and economic challenges to ISOs. According to recent advances in smart grid technologies, Demand Response Programs (DRPs) are expected to facilitate the integration of intermittent renewable energy resources into the grid. In this paper, DRPs are considered as an option to reduce and manage renewable energy resources uncertainty. On this basis, this paper proposes a flexible load approach with the application to wind power grid integration. In this context, and in order to model the reality of the power market, a network constrained unit commitment problem associated with DRPs is presented to clear the market transactions. Simulation results show that utilizing DRPs can improve grid integration of wind power, while making significant economic and technical benefits for the system.