Day-ahead Scheme Research Articles

A novel day-ahead scheduling model to unlock hydropower flexibility limited by vibration zones in hydropower-variable renewable energy hybrid system

Giving full play to the flexibility of hydropower and integrating more variable renewable energy (VRE) is of great significance for accelerating the transformation of China's power energy system. For middle- to large-sized hydropower units, its irregular vibration zone (VZ) is a major factor affecting its flexibility, because VZs limits the power output and adjustment range. In the existing studies on hydropower unit commitment and flexibility scheduling, VZs only limits the output of hydropower units, while the impact on the flexibility adjustment ability of units is ignored. The hydropower flexibility in day-ahead scheme is overestimated, which increases the risk of system flexibility shortage causing power curtailment and load loss. In this study, a novel day-ahead scheduling model considering the flexibility limited by the VZs and the probability of flexibility shortage is constructed for the day-ahead generation scheme of hydropower-VRE hybrid generation system (HVHGS) to optimize the target load, VRE output and hydropower unit commitments. The impaction of the VZs on hydropower flexibility is firstly modeled by chance constraints and stochastic programming method. Moreover, a data-driven model based on machine learning and an efficient solving approach based on successive linear programming is carry out to describe the uncertainty of VRE output more realistically and ensure the timeliness of optimization scheme, respectively. The proposed model is applied to a real hydropower station in the Hongshui River Basin in China. In 16 representative scenarios, the proposed model can complete the optimization in an acceptable time, with a maximum of 444.57 s. Compared with the traditional interval optimization model, the proposed model effectively improves the flexibility supply capacity of hydropower in the day-ahead scheme. The maximum reduction value of flexibility shortage probability and expectation reach 98.77% and 442.66 MW, respectively. In particular, the flexibility of the model is most obvious under heavy load demand in flood season, and it is almost not at the cost of daily target load adjustment, which provides practical reference for decision makers.

Demand-side energy flexibility estimation for day-ahead models

A flexibility estimation model is proposed to evaluate the immediate aggregate flexibility response in a day-ahead scheme. The proposed model schedules a set of appliances and calculates the aggregated flexibility according to the energy and flexibility prices. The temporal granularity of the problem can be adjusted to evaluate the flexibility for the next 15 min or 4 h, using an alternative flexibility scenario approach to evaluate immediate flexibility available. The alternative flexibility scenario is a limited number of time step used to evaluate whether the estimated flexibility is available for a defined time window in each time step. New flexibility constraints were introduced to evaluate the flexibility and the rebound effect for air conditioners, pool pumps, and electric water heater, according to the alternative flexibility scenario. The model was tested under different energy price schemes and flexibility requirement durations to observe how the price signals influenced the flexibility offered. The results show how a model that considers extended flexibility, even if it does not consider the total duration requirement, can offer a more accurate flexibility response than a flexibility estimation without an extended flexibility response.

Optimal operation of battery exchange stations for electric vehicles

Due to environmental and energy security concerns, low emission vehicles present a vital necessity for clean transportation. In particular, electric vehicles (EVs) are the most promising solution due to the fact that the electrical power system is the most ready infrastructure to supply their requirement. Two possible energy delivery solutions to the EVs, namely the charging stations and the battery exchange stations (BESs) are the focus of research nowadays. In this paper, a new optimal operation approach is proposed for the BESs. The proposed new model determines the optimal charging, discharging, and exchange decisions for the battery stock throughout the day taking into consideration the customers’ arrivals, the variations in the grid price, the grid connection limitations, and the self-degradation of the batteries. The objective of the proposed approach is to maximize the BES owner profit while satisfying the EV owners’ requests. The BES operation optimization problem is formulated as mixed-integer programming (MIP) problem and is solved as a day-ahead scheme. The performance of the BES is compared to conventional EV charging stations, where the BES shows superior customer satisfaction and higher profit.

Agent-Based Modeling in Electrical Energy Markets Using Dynamic Bayesian Networks

Due to uncertainties in generation and load, optimal decision making in electrical energy markets is a complicated and challenging task. Participating agents in the market have to estimate optimal bidding strategies based on incomplete public information and private assessment of the future state of the market, to maximize their expected profit at different time scales. In this paper, we present an agent-based model to address the problem of short-term strategic bidding of conventional generation companies (GenCos) in a power pool. Based on the proposed model, each GenCo agent develops a private probabilistic model of the market (using dynamic Bayesian networks), employs an online learning algorithm to train the model (sparse Bayesian learning), and infers the future state of the market to estimate the optimal bidding function. We show that by using this multiagent framework, the agents will be able to predict and adapt to approximate Nash equilibrium of the market through time using local reasoning and incomplete publicly available data. The model is implemented in MATLAB and is tested on four test case systems: two generic systems with 5 and 15 GenCo agents, and two IEEE benchmarks (9-bus and 30-bus systems). Both the day-ahead (DA) and hour-ahead (HA) bidding schemes are implemented. The results show a drop in market power in the 15-agent system compared to 5-agent system, along with a Pareto superior equilibrium in the HA scheme compared to the DA scheme, which corroborates the validity of the proposed decision making model. Also, the simulations show that introduction of an HA decision making stage as an uncertainty containment tool, leads to a more stable and less volatile price signal in the market, which consequently results in flatter and improved profit curves for the GenCos.

Game Theoretic Model Predictive Control for Distributed Energy Demand-Side Management

Distributed energy generation and storage are widely investigated demand-side management (DSM) technologies that are scalable and integrable with contemporary smart grid systems. However, prior research has mainly focused on day-ahead optimization for these distributed energy resources while neglecting forecasting errors and their often detrimental consequences. We propose a novel game theoretic model predictive control (MPC) approach for DSM that can adapt to real-time data. The MPC-based algorithm produces subgame perfect equilibrium strategies for distributed generation and storage with perfect forecasting information, and is shown to be more effective than a day-ahead scheme when mean forecasting errors greater than 10% are present. This robust and continuous MPC approach reduces effective forecasting errors, and in doing so, achieves greater electricity cost savings and peak to average demand ratio reduction than the day-ahead optimization scheme.