Highly wind penetrated future power system will couple to the variabilities and nonlinear correlations of wind. Reliable wind power forecasting (WPF) for a region is critical to the security and economics of the power system operation. Therefore, this paper proposes a multiscale WPF method by establishing a multi-to-multi (m2m) mapping network and the use of stacked denoising autoencoder (SDAE). The concerned forecast time horizon is 24-72 hours. First, multi-NWPs in a region are corrected based on SDAE to generate better inputs for the following regional WPF. Second, a number of SDAEs with diverse model parameters and input features are integrated into ensemble SDAE for predicting the wind power generated from various wind farms in a region. Two sets of data are utilized in this case study to validate the proposed method. The results show that the proposed m2m mapping and SDAE are able to capture the real correlations of wind at multiple sites, and outperform the other counterparts in terms of multi-NWPs correction as well as the WPF for both the region and individual concerned wind farm. Moreover, the ensemble SDAE performs better than any other individual regional WPF model.

Coordinating multiple energy systems (MES) enables the synergies of different energy sectors to be exploited. The concept of low-carbon development changes planning approaches for both the district level and the multi-regional level of MES. This paper proposes a bi-level expansion planning model of MES that considers the emission constraints under a decentralized approach. The upper-level model investigates the optimal planning scheme for integrated power and natural gas networks in the multi-regional MES. The lower-level model examines the optimal energy supply configuration of multiple energy carriers in the district MES based on the energy hub (EH) modeling approach. The carbon emission flow (CEF) model is used to allocate the overall carbon emission cap among district MES from the consumption-based perspective and to coordinate the planning of the district level and the multi-regional level. An illustrative example based on a 6-node MES verifies the effectiveness of the proposed model. Finally, a realistic case study based on an MES in northern China is implemented to show the effects of carbon emission constraints on the planning of real-world energy systems.

Taking advantage of various flexible resources to reduce or even eliminate renewable energy (RE) curtailment is an important issue in the operational planning of future power systems. Building a highly compatible flexibility evaluation method that incorporates a clear relation between flexibility indices and RE curtailment is crucial to addressing this issue. A novel flexibility evaluation methodology is proposed based on the probabilistic distribution of flexibility adequacy. The novel indices can reflect the direction, amount, frequency, and consequence of lack of flexibility and also have a linear relationship with RE curtailment, features that are beneficial for planners aiming to determine the type of flexible resources needed and to quantify their capacity. The conditional probability distribution functions of different types of flexible resources are first built. Then, a unified form of flexibility on different time-scales is proposed to represent and calculate their contributions to flexible indices using conditional probability convolution. The evaluation method is validated in a combined heat-and-electricity supply system. Four types of flexible resources are studied to demonstrate the quantitative capability of the proposed method in planning, including conventional units, electricity storage devices, heat storage devices, and electric boilers.

The integration of variable renewable energy (VRE) generation, i.e. wind power and solar photovoltaic, brings significant uncertainty for the power system operation. Different with VRE techniques, concentrating solar power (CSP) is an appealing renewable generation technology due to its dispatch-ability through the use of thermal energy storage (TES) and is thus expected to play a significant role in high renewable energy penetrated power systems. In this paper, we propose a look-ahead stochastic unit commitment model to operate power systems with CSP under high renewable energy penetration. It has a three-stage structure. The first stage optimizes the operational decisions in a day-ahead framework based on forecasts; the second stage minimizes the expected generation cost for possible realizations in the real-time; and the third stage accounts for look-ahead operation in future operating days. This paper has a dual purpose: 1) exploring how CSP plants operate in high renewable penetrated power systems and 2) analyzing the benefits of CSP in accommodating VRE generation. A case study on a modified IEEE RTS-79 system with actual solar and wind power data is provided to validate the proposed method.

The rotational inertia and primary frequency response capabilities of power systems are declining as synchronous generators (SGs) are replaced by inverter-based variable renewable energy (VRE) resources. Therefore, when a high VRE proportion is present, it becomes essential to include frequency security constraints in power system planning. Under this circumstance, the decline in SGs would deteriorate the system frequency response performance; thus, inverter-based resources are supposed to provide frequency response support. This paper presents a frequency-constrained stochastic planning method for high VRE share systems. Frequency response support from wind farms is considered in the planning process for the first time, and the unique characteristics of wind power support are addressed. Specifically, the uncertainty of and variations in wind power support capabilities are modeled from the field-measured data, and the optimal combination of various support schemes can be determined by introducing binary variables with novel linear constraints into the planning process. Moreover, the nonlinear frequency constraints are transformed and embedded in the planning model by the proposed computationally efficient adaptive piecewise linearization method. The planning is formulated as a stochastic optimization model to consider the VRE power uncertainty. Finally, case studies on the IEEE 30-bus system validate the proposed method, with results indicating time savings of 22%. Further, compared with the case that only SGs provide frequency support, the results based on the East China system demonstrate that considering wind power support can improve the VRE share from 49.5% to 79.7%.