• Energy Research

Today, wind power is becoming an important energy source for the future development of electric energy due to its clean and environmentally friendly characteristics. However, due to the uncertainty of incoming wind, the utilization efficiency of wind energy is extremely low, which means the problem of wind curtailment becomes more and more serious. To solve the issue of wind power large-scale consumption, a two-stage stochastic optimization model is established in this paper. Different from other research frameworks, a novel two-side reserve capacity mechanism, which simultaneously takes into account supply side and demand side, is designed to ensure the stable consumption of wind power in the real-time market stage. Specifically, the reserve capacity of thermal power units is considered on the supply side, and the demand response is introduced as the reserve capacity on the demand side. At the same time, the compensation mechanism of reserve capacity is introduced to encourage generation companies (GENCOs) to actively participate in the power balance process of the real-time market. In terms of solution method, compared with the traditional k-means clustering method, this paper uses the K-means classification based on numerical weather prediction (K-means-NWP) scenario clustering method to better describe the fluctuation of wind power output. Finally, an example simulation is conducted to analyze the influence of reserve capacity compensation mechanism and system parameters on wind power consumption results. The results demonstrate that with the introduction of reserve capacity compensation mechanism, the wind curtailment quantity of the power system has a significant reduction. Besides, the income of GENCOs is gradually increasing, which motivates their enthusiasm to provide reserve capacity. Furthermore, the reserve capacity mechanism designed in this paper promotes the consumption of wind power and the sustainable development of renewable energy.

Renewable energy resources (RESs) play an important role in the upgrading and transformation of the global energy structure. However, the question of how to improve the utilization efficiency of RESs and reduce greenhouse gas emissions is still a challenge. Combined heating and power (CHP) is one effective solution and has experienced rapid development. Nevertheless, with the large scale of RESs penetrating into the power system, CHP microgrid economic operation faces great challenges. This paper proposes a CHP microgrid system that contains renewable energy with considering economy, the environment, and system flexibility, and the ultimate goal is to minimize system operation cost and carbon dioxide emissions (CO2) cost. Due to the volatility of renewable energy output, the fuzzy C-means (FCM) and clustering comprehensive quality (CCQ) models were first introduced to generate clustering scenarios of the renewable energy output and evaluate the clustering results. In addition, for the sake of improving the flexibility and reliability of the CHP microgrid, this paper considers the battery and integrated energy demand response (IEDR). Moreover, the strategy choices of microgrid operators under the condition of grid-connected and islanded based on environment and interest aspects are also developed, which have rarely been involved in previous studies. Finally, this stochastic optimization problem is transformed into a mixed integer linear programming (MILP), which simplifies the calculation process, and the results show that the operation mode under different conditions will have a great impact on microgrid economic and environmental benefits.

Abstract Demand-side resources play a significant role in enhancing energy efficiency and decarbonization. Performing demand curtailment will psychologically disturb end-customers' comfort and affect decision-making. The penetration of battery energy storage systems (BESSs) in electricity grids introduces another response resource to the grid operator (GO). Therefore, it's important to investigate the effect of different customer psychological factors (CPFs) on incentive-based demand response (IBDR) strategy in the system with diversified response resources including BESSs. Behavioral economics (BE) interprets individual behavior from psychology and provides insights to behavior modeling. Therefore, this paper applied BE to incorporate CPFs, such as the endowment effect and time-discounting effect. Furthermore, to bring the value of CPFs to the system level, an IBDR model considering CPFs and BESSs (CE-IBDR) is proposed by following the Stackelberg game theory. Upon the participation of load aggregators (LAs) and BESSs operator (EO) in IBDR, the model extends two-party hierarchical levels to four-party spinning from the GO, EOs, LAs and end-customers by extending the two-party Stackelberg game to a two-loop Stackelberg game. Results show that without incorporating CPFs into the model will result deviation in interpreting customer behavior. BESSs is preferred reponse resource than load reduction due to the pressure to incentive end-customers with high endowment valuation.

Abstract Integrated energy systems (IESs) as the widely concerned multi-energy systems have significant contributions to enhancing energy utilization efficiency and renewable energy (RE) consumption. With the increasing proportion of RE, integrated energy aggregators (IEAs), who coordinate IESs with multiple generators, will have more important roles in the future smart grid. This paper presents a Bayesian evolutionary game (BEG) method to study the optimal supply strategy for generating units of different energy types to maximize their own profits in an unregulated power market. The competition among IEAs lowers their willingness to share their information and restricts the profit themselves. Given this information asymmetry, the interaction of three types of generators in IESs is captured by the Bayesian game to transform the incomplete game into a complete game with imperfect information. Given the dynamic of the spot market, this paper combines the Evolutionary game theory with Bayesian theory to study the symbiotic evolution among them. Simulations are introduced to examine the asymptotic stability of various evolutionary stabilization strategies. The results verify the effectiveness of the proposed model. Finally, the implications of different renewable energy penetration, market-clearing rules, market share, and the market supply-demand ratio on IEAs’ offering behavior are explored by applying the experimental economics principle.

With renewable energy sources (RESs) highly penetrating into the power system, new problems emerge for the independent system operator (ISO) to maintain and keep the power system safe and reliable in the day-ahead dispatching process under the fluctuation caused by renewable energy. In this paper, considering the small hydropower with no reservoir, different from the other hydro optimization research and wind power uncertain circumstances, a day-ahead scheduling model is proposed for a distributed power grid system which contains several distributed generators, such as small hydropower and wind power, and energy storage systems. To solve this model, a two-stage stochastic robust optimization approach is presented to smooth out hydro power and wind power output fluctuation with the aim of minimizing the total expected system operation cost under multiple cluster water inflow scenarios, and the worst case of wind power output uncertainty. More specifically, before dispatching and clearing, it is necessary to cluster the historical inflow scenarios of small hydropower into several typical scenarios via the Fuzzy C-means (FCM) clustering method, and then the clustering comprehensive quality (CCQ) method is also presented to evaluate whether these scenarios are representative, which has previously been ignored by cluster research. It can be found through numerical examples that FCM-CCQ can explain the classification more reasonably than the common clustering method. Then we optimize the two stage scheduling, which contain the pre-clearing stage and the rescheduling stage under each typical inflow scenario after clustering, and then calculate the final operating cost under the worst wind power output scenario. To conduct the proposed model, the day-ahead scheduling procedure on the Institute of Electrical and Electronics Engineers (IEEE) 30-bus test system is simulated with real hydropower and wind power data. Compared with traditional deterministic optimization, the results of two-stage stochastic robust optimization structured in this paper, increases the total cost of the system, but enhances the conservative scheduling strategy, improves the stability and reliability of the power system, and reduces the risk of decision-making simultaneously.