Achieving high renewable energy penetrated power systems requires considerable operational flexibility to hedge the variability and uncertainty of variable renewable energy (VRE) generation. Compared with VRE sources, concentrating solar power (CSP) is an emerging controllable renewable generation technique that utilizes solar thermal power to generate electricity. The operational dispatchability of CSP would contribute to the power system transition toward high renewable penetration. In this paper, we explore how the generation portfolio will change toward high renewable energy penetrations, how much cost is involved, and what role CSP will play in realizing a high renewable energy penetrated power system. This study relies on a stochastic two-stage generation and transmission expansion planning model with CSP plants. The model captures the uncertainty and variability of renewable generation and the flexibility limits of thermal plants. With the target of achieving a renewable-dominated minimum-cost system with an expected renewable energy penetration level, the investments of both generation and transmission facilities are optimized. A case study on IEEE test systems with renewable technology cost data in 2050 is performed to analyze the value of CSP toward high renewable energy penetrated power systems.

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.

With increasing wind penetration, wind power ramps (WPRs) are currently drawing great attention to balancing authorities, since these wind ramps largely affect power system operations. To help better manage and dispatch the wind power, this paper develops a data-driven probabilistic wind power ramp forecasting (p-WPRF) method based on a large number of simulated scenarios. A machine learning technique is first adopted to forecast the basic wind power forecasting scenario and produce the historical forecasting errors. To accurately model the distribution of wind power forecasting errors (WPFEs), a generalized Gaussian mixture model (GGMM) is developed and the cumulative distribution function (CDF) is also analytically deduced. The inverse transform method based on the CDF is used to generate a large number of forecasting error scenarios. An optimized swinging door algorithm (OpSDA) is adopted to extract all the WPRs from the complete set of wind power forecasting scenarios. The p-WPRF is generated based on all generated scenarios under different weather and time conditions. Numerical simulations on publicly available wind power data show that the developed p-WPRF method can predict WPRs with a high level of reliability and accuracy.

The high penetration of renewable energy (RE) significantly impacts the power system frequency stability for the following reasons: first, the replacement of conventional thermal generator by RE such as wind and solar degrades the frequency control capability; second, the variability of wind and solar imposes more challenges on the frequency stability. The current unit commitment (UC) program in the industrial practice cannot fully provide frequency reserve requirement to maintain the satisfactory frequency performance and the frequency reserves are not fully compensated through the existing market scheme. In this paper, a security constrained unit commitment (SCUC) model with primary and secondary frequency reserve is proposed to co-optimize the energy and frequency reserve to provide satisfactory frequency performance. Post-contingency transmission constraints are enforced to account for the deliverability of the frequency reserve. The locational based reserve pricing is proposed to differentiate the compensating prices for reserves procured at various locations. A five-bus system and a 118-bus system are used to test the proposed model. Frequency dynamic simulation is also implemented to illustrate the frequency performance improvement based on the proposed method in terms of various frequency metrics.

This paper proposes a multi-energy trading framework for a hybrid-renewable-to-H2 provider (HP) to coordinate the interaction and trading of electricity and H2 while promoting the efficient accommodation of renewable energy resources (RESs). In this framework, the HP can harvest hybrid RESs for green H2 production based on electrochemical effects of biomass electrolysis, and procure stacked profits from both the electricity and H2 markets by the flexibility of electricity-H2 conversion. A Vickrey auction-based pricing mechanism is developed to determine the trading price and quantity of H2 while eliciting truthful offers and bids in a competitive H2 market. Then, a single-leader-multiple-follower Stackelberg game with an iterative solution algorithm is formulated to capture the interactions between the H2 auctioneer and hydrogen fueling stations (HFSs) for achieving the win-win goal. Furthermore, a hybrid-renewable-to-H2 production and control method is proposed for the HP to raise the production efficiency of green H2 and suppress large fluctuations in electrolysis current caused by RES uncertainties. Comparative studies have validated the superiority of the proposed methodology on economic performance and RES accommodation.

Unbalanced operation of a three-phase distribution system could incur more power losses, compared with the balanced operation. So far, the phase information of users in low voltage areas is not recorded by power utilities and the three-phase unbalance degree is very significant in some low voltage distribution systems. Given this background, this letter presents an integrated method for solving related issues including user phase identification based on spectral clustering and three-phase unbalance mitigation, and a Mixed Integer Linear Programming (MILP) model is then formulated. Some actual cases in Zhejiang province, China, are utilized to verify the effectiveness of the presented phase identification algorithm, and simulation results show that the three-phase unbalance can be significantly mitigated.