The system inertia is the inherent abi lity of the online synchronous machines to oppose sudden changes in generation or load. Wi th increasing share of non-synchronous generation technologies (e.g. wind and solar generation) system inertia could decrease. One of the options to address system inertia decline is to increase frequency response reserves to achieve a satisfactory frequency control. This paper discusses a new prototype tool developed in Electric Reliabil ity Council (ERCOT) to forecast system inertia and to evaluate adequacy of frequency response reserve s. The performance of this prototype tool is also evaluated.

Wind generation resources (WGRs) are the fastest growing energy resources throughout the world. An accurate wind forecasting is critical to the integration of a large amount of wind generation units into the grid operations. This paper presents an ensemble machine learning based method to forecast wind power production, which uses both the wind generation forecasted by a numerical weather prediction (NWP) model and the meteorological observation data from weather stations. In this way, it takes advantage of the atmosphere models while capturing spatial and temporal correlation between meteorological observations at different locations. Three machine learning algorithms (artificial neural network (ANN), support vector regression (SVR), Gaussian process (GP)) are proposed to synthesize the meteorological data and the prediction from NWP. An ensemble forecast is then created by blending the results derived from three algorithms through a Bayesian model average (BMA). The performance of this ensemble forecast has been validated by the 2-year operational data collected at Electricity Reliability Council of Texas (ERCOT).

This paper presents the design of a new primary frequency control (PFC) market for hosting frequency response reserve (FRR) offers from both generators and loads. Traditionally, FRR is provided by synchronous generators. Advanced control and monitoring technologies have enabled loads to provide fast and discrete PFC, which is complementary to the slow and continuous governor response provided by synchronous generators. In this paper, the performance of the PFC provided by the load is benchmarked by the equivalent amount of PFC provided by the synchronous generators at each typical system inertia condition using the actual dynamic network models and operation data of the Electric Reliability Council of Texas (ERCOT) system. Then, a new real-time PFC market mechanism is proposed to accept and value the PFC offers from both generators and loads. A few case studies are presented to illustrate the new market mechanism and demonstrate its effectiveness for mitigating price spikes. The results of this paper are applicable to other low-inertia power grids similar to the ERCOT system for procuring ancillary services that are essential to host high penetration of renewable resources.

Carbon emission rights (CER) are a market mechanism adopted to reduce carbon dioxide emissions. In this paper, a decentralized market model integrating electricity and CER trading is established for a microgrid. The proposed trading model not only satisfies the demand for transactions but also ensures the constraint of total carbon emissions for the microgrid. Energy storage (ES) is introduced to balance loads more economically, and the constraint specifying that the ES cannot be simultaneously charged and discharged is proven to be satisfied automatically, ensuring the convexity of the model. Furthermore, by inserting local trackers to global constraints for each node, a scalable fully distributed algorithm is designed to solve the model locally for both global equality and inequality constraints. The proposed algorithm decomposes the arithmetic demand to each user without intermediate agents, which can effectively reduce the cost and ensure the transparency of trading. The algorithm is also proven to be convergent. Numerical results verify the effectiveness of the proposed market model.

Air conditioners (ACs) are widely considered as good candidates to provide operating reserve. Demand response rebound, i.e., the rebound peak of aggregate power, may exist when ACs are controlled by changing the set point temperature. The rebound peak during the recovery period, named lag rebound, may cause significantly higher demand than that prior to the reserve deployment event. The rebound peak during the reserve deployment period, named lead rebound, is rarely considered in previous researches but will constrain the duration time to a short period (e.g., 10 min), which greatly limits the utilization of ACs. This paper proposes an optimal sequential dispatch strategy of ACs to mitigate the lead-lag rebound, and thus realize flexible control of the duration time from minutes to several hours. To quantify the effects of lead-lag rebound, a capacity-time evaluation framework of the operating reserve is developed. On this basis, ACs are grouped to be dispatched in sequence to mitigate the lead-lag rebound. The co-optimization of sequential dispatch on the capacity dimension and time dimension forms a mixed-integer nonlinear bilevel programming problem, in which the consumers' thermal comfort is also guaranteed. Case studies are conducted to validate the proposed strategy.

This paper presents a multi-period active distribution network planning (ADNP) with distributed generation. While subject to both planning and operation constraints, the objective function of the proposed ADNP is to minimize the total cost of investment and operation. The paper proposes a multi-stage stochastic optimization model to address the uncertainties associated with the distributed generation (DG). In order to characterize the uncertainties over several periods, the decisions are made sequentially by only using the present-stage information. Furthermore, a nested decomposition method is proposed which applies the stochastic dual dynamic integer programming (SDDIP) method to address computational intractabilities. The presented numerical results and discussions on a 33-bus distribution system verify the effectiveness of the proposed model and its solution method.