Stochastic Day-ahead Scheduling Research Articles

Stochastic Day-Ahead Scheduling of Distributed Energy Resources: A Meta Modeling Approach

Stochastic Day-Ahead Scheduling of Distributed Energy Resources: A Meta Modeling Approach

Computational intelligence based PEVs aggregator scheduling with support for photovoltaic power penetrated distribution grid under snow conditions

• A snow loss-aware deep learning-based short-term PV yield predictor is developed. • Stochastic day-ahead scheduling of an independent PEVs aggregator is investigated. • A novel local balancing service offered by the PEVs aggregator to DSO is proposed. • The service benefits PEV owners through providing extra energy demand of the grid. This paper addresses the issue of optimal day-ahead scheduling of a plug-in electric vehicles (PEVs) aggregator that participates in the electricity market and offers an out-of-market balancing service to the local renewable power penetrated distribution system in a snow-prone area. The proposed balancing service provides a reliable source of flexibility for the extra real-time energy demand of the distribution system operator (DSO) which originates from the difference between its day-ahead bids and the actual demand. The problem is investigated on a snowy day when the DSO's day-ahead decisions encounter more uncertainty due to the considerable effect of snow loss on the DSO's photovoltaic plant performance. The aggregator's scheduling is formulated as two-stage stochastic programming which minimizes the PEVs’ charging cost. Monte Carlo simulation and K-means clustering are implemented to generate scenarios of driving patterns and real-time energy market prices, respectively. Offering the balancing service requires day-ahead predictions of the photovoltaic power and the grid load demand which are modeled using long short-term memory networks. The problem is formulated as mixed-integer linear programming. The results show that the proposed scheduling approach reduces the PEVs’ charging cost by 53% and guarantees the grid normal operation. Moreover, the balancing service can reduce the expected PEVs’ charging cost and the DSO's real-time cost by 12% and 14%, respectively. © 2017 Elsevier Inc. All rights reserved.

Combined static and dynamic dispatch of integrated electricity and heat system: A real-time closed-loop demonstration

Coupling the electricity and heating systems introduce opportunities to handle the intermittent nature of an increasing share of renewable power generation. Coordination of units in the coupled systems requires careful static dispatch towards an optimization objective. As the uncertainties in renewable forecasting are reduced towards operation time, operators can perform dispatch numerous times to balance the determined mismatches. With higher shares of renewable, sudden changes in power generation due to changing weather can cause a power imbalance that disturbs the system frequency. To handle such hazards, dynamic dispatch in the form of frequency control is necessary. Recent literature in integrated electricity and heating systems (IEHS) is studied in this work to combine the static and dynamic dispatch of units. A two-stage stochastic day-ahead scheduling method combined with a model predictive control (MPC) -based real-time optimal operation method that allocates both following and regulating reserves is used as the static dispatch of the IEHS. The allocated reserves constrain the performance of the dynamic dispatch by limiting the control capacity. In this work, an MPC coordinated frequency control of small- and large-scale heat pumps is considered as the dynamic dispatch of the IEHS. The performance of the dynamic dispatch is evaluated through a real-time closed-loop simulation platform, where the system conditions of the static dispatch are used as the steady-state conditions. Results show that despite challenging communication conditions, the frequency control of small- and large-scale heat pumps improves frequency nadir conditions after a power imbalance.

Stochastic day-ahead scheduling of irrigation system integrated agricultural microgrid with pumped storage and uncertain wind power

Agricultural microgrid provides a promising solution for energy supply of rural areas in a cost-effective way. In this paper, the principle of wind-pumped storage integrated agricultural microgrid to fulfill both the electric and water load demand is explored. From the perspective of risk aversion, the indexes of expected power not served (EPNS) and expected power curtailment (EPC) are derived to evaluate the uncertain wind power. Then, a stochastic day-ahead scheduling model of wind-pumped storage power system and irrigation system is proposed to minimize the total operation cost whilst achieve the peak load shaving by taking the advantage of wind-pumped storage compensation. The proposed model is a complex optimization problem that takes into account distributed generations, electrical and water demand, turbine flow rate, and irrigation time and volume. We propose a intraspecific competition (IC) based evolutionary predator and prey strategy (EPPS) to solve the model. Finally, simulation results conducted on an agricultural microgrid have verified that, compared to previous works in the literature, the proposed model can effectively improve the utilization of wind power, reduce the operation cost of microgrid as well as smooth load curve. Additionally, the proposed algorithm can get a more competitive solution than other recently developed algorithms.

Transactive energy management for optimal scheduling of interconnected microgrids with hydrogen energy storage

In recent years, renewable energy sources (RESs) have attracted substantial attention due to carbon-free and cost-effective advantages that have made them one of the main sources of energy generation in the modern structure of the power grid. This paper proposes the stochastic day-ahead scheduling model for optimal energy management of renewable-based microgrids. In this paper, each microgrid is equipped with 100% RESs including the PV system and wind turbine for full pollutant-free energy generation while the hydrogen energy storage (HES) system is used for alleviating the intermittences of the RESs aiming to dynamically balance the energy during a day. To model the fluctuations such as day-ahead market price in the microgrids, the autoregressive integrated moving average and fast forward selection methods are exerted for scenario production and reduction, respectively. Transactive energy as a sustainable and reliable technique is considered for controlling and coordinating energy sharing among the microgrids and the energy network for dynamic energy balancing in the deregulated environment. For energy management in the demand-side, the price and load response schemes are presented, aiming to revise the consumers' patterns in energy consumption in line with balancing energy and minimizing the microgrids’ energy cost. The effectiveness of the suggested model is validated using the modified IEEE 24-bus case study. The realistic modeling of the system based on the proposed model has led to an 8.51% increment in energy cost.

A Stochastic Two-Stage Model for the Integrated Scheduling of the Electric and Natural Gas Systems

The inherent coupling of the electric and natural gas systems due to the operation of gas generating units and power-to-gas facilities, along with the uncertainties faced in both systems due to the variability in electricity and gas demand and the vastly increasing volatile renewable injections, create an imperative need to schedule and operate the two systems in a coordinated manner. In this paper a new model for the fully integrated stochastic day-ahead scheduling of electric and gas systems is presented, coping with the uncertainties of both systems. The stochastic parameters comprise the electricity demand and the renewable injections, which collectively create several net electricity load scenarios, and the gas residential/industrial demand. The integrated scheduling problem concerns a unit commitment for the electricity problem, amended with additional constraints imposed by the underlying natural gas transmission system considering steady-state flow. A two-stage stochastic programming model is devised, having as second stage the possible realizations of net electricity load and gas demand in real-time. The model is tested in medium-size real-world test systems – the Greek electricity and gas systems – deriving useful insights on the advantages of the integrated stochastic scheduling versus the deterministic scheduling of the electricity and gas systems.

Application of modified progressive hedging to stochastic unit commitment in electricity-gas coupled systems

The increasing number of gas-fired units has significantly intensified the coupling between electric and gas power networks. Traditionally, nonlinearity and nonconvexity in gas flow equations, together with renewable-induced stochasticity, resulted in a computationally expensive model for unit commitment in electricity-gas coupled integrated energy systems (IES). To accelerate stochastic day-ahead scheduling, we applied and modified Progressive Hedging (PH), a heuristic approach that can be computed in parallel to yield scenario-independent unit commitment. Through early termination and enumeration techniques, the modified PH algorithm saves considerable computational time for certain generation cost settings or when the scale of the IES is large. Moreover, an adapted second-order cone relaxation (SOCR) is utilized to tackle the nonconvex gas flow equation. Case studies were performed on the IEEE 24-bus system/Belgium 20-node gas system and the IEEE 118-bus system/Belgium 20-node gas system. The computational efficiency when employing PH is 188 times that of commercial software, and the algorithm even outperforms Benders Decomposition. At the same time, the gap between the PH algorithm and the benchmark is less than 0.01% in both IES systems, which proves that the solutions produced by PH reach acceptable optimality in this stochastic UC problem.

Stochastic Day-Ahead Scheduling of Integrated Energy Distribution Network With Identifying Redundant Gas Network Constraints

Considering uncertainties from wind power and day-ahead market price, this paper proposes a scenario-based two-stage model for the day-ahead scheduling of integrated energy distribution network which couples various energy systems such as electricity, heat, and natural gas. However, gas network constraints pose a great challenge to the computation because the piecewise linear approximation (PLA) method linearizes the nonlinear flow-pressure constraints (i.e., Weymouth equations) by adding a large number of additional auxiliary variables and constraints into the model, which increases the complexity of the model. To address this problem, we propose a sufficient condition for identifying redundant gas network constraints. We also develop a bounds tightening strategy that combines Weymouth equation relaxation and an optimality-based bounds tightening method to mitigate the conservatism of the proposed sufficient condition. Numerical case studies show that the proposed method can greatly improve the computational performance of the optimization model, making it superior to the conventional PLA method. For a larger-scale test system, the improvement of computational efficiency is more significant.

Impact of uncertainty and correlation on operation of micro-integrated energy system

The uncertainty and correlation of energy sources and loads bring challenges to the operation of integrated energy system (IES). In this article, a two-stage stochastic day-ahead scheduling problem on a micro-IES (MIES) is analyzed, and the correlation model of electricity and heat loads is presented as a constraint. Most importantly, for the new MIES with less or no historical data, a combined scenario and Monte-Carlo (MC) method is presented to manage the uncertainties of solar photovoltaic (PV) generation, electricity and heat loads, as well as the correlation between stochastic electricity and heat loads. The impact of uncertainty and correlation on the performance and optimal operation of the MIES are investigated under a two-stage stochastic day-ahead scheduling model. Case studies show that the proposed combined scenario and MC method is suitable for scenario generation with the consideration of uncertainty and correlation in the new MIES. The varying range of the total cost of the MIES is influenced by the MC sampling and scenario reduction number, which reflect the impact of various uncertainties. It is also demonstrated that considering correlation between the electricity and the heat loads can evaluate various cost in the MIES more accurately.

Stochastic day-ahead scheduling of multiple energy Carrier microgrids with demand response

Microgrids are indispensable components of active energy systems that supply diverse electrical and thermal demands. A microgrid is composed of distributed energy resources (DER) including renewable resources, combined heat and power generation (CHP) and conventional generation resources that rely on fossil fuels. Energy hubs in microgrids facilitate the conversion of different types of energy resources. The coupling among natural gas and electricity distribution networks introduces new challenges to the short-term operation planning of microgrids. In this paper, a two-stage stochastic optimization problem is formulated for the short-term operation planning of microgrids with multiple-energy carrier networks to determine the scheduled energy and reserve capacity. The problem is formulated as a mixed integer linear programming problem in which the objective function is to minimize the expected operation cost in the short-term operation horizon. The uncertainties in the renewable generation including the wind and solar photovoltaic generation, and electrical and thermal demands are captured by introducing scenarios with respective probabilities. The proposed solution framework ensures the reliability and security of energy supply in multiple scenarios. The advantage of capturing the interdependence among the electricity and natural gas systems to promote energy efficiency is presented. Furthermore, the effectiveness of demand response programs to reduce the operation costs and improve the security measures is investigated. The sensitivity of the operation costs to the variation of natural gas flow and congestion in pipelines and energy prices is addressed to highlight the interdependence among natural gas and electricity infrastructure systems.

A two-stage framework for multiobjective energy management in distribution networks with a high penetration of wind energy

The integration of renewable energy sources (RESs) in distribution networks has brought great challenges to the volt/var management due to their intermittency and volatility. This paper proposes a two-stage energy management framework of distribution networks to facilitate the accommodation of high wind energy penetration. In the proposed framework, the volt/var management problem is formulated and decomposed as a two-stage energy scheduling optimization model with different time frames considering the uncertainties of wind energy and load forecasts. In the first stage, a scenario-based stochastic day-ahead scheduling model is formulated to optimize the 24-h charging/discharging scheme of energy storage system (ESS) and power generation of diesel generator (DG) in order to minimize the expected operation cost. Based on the stochastic optimal scheduling results in the first stage, the second stage implements the multiobjective volt/var optimization (VVO) to determine the optimal real-time operation of volt/var control devices, considering the costs of adjusting the control devices (CACDs). The proposed method has been fully evaluated and benchmarked on a 69-bus distribution network under various operational scenarios to demonstrate its superiority on various performance metrics and further confirm its effectiveness and efficiency for distribution networks to accommodate a high penetration of wind energy.

Stochastic Scheduling of Integrated Energy Systems Considering Wind Power and Multienergy Loads Uncertainties

AbstractThis paper proposes a stochastic day-ahead scheduling framework for integrated energy systems (IES) considering uncertain wind power generation and multienergy loads. The structure and mode...

Optimal Dispatch of Responsive Loads and Generic Energy Storage Units in a Co-Optimized Energy and Reserve Market for Smart Power Systems

Responsive loads and energy storage units can play an important role to provide an economic and reliable operation of future energy systems. In this article, a stochastic energy and reserve market clearing scheme is presented considering demand response programs and energy storage devices. The approach is proposed to deal with stochastic and non-dispatchable renewable sources with high levels of penetration in the energy system. A two-stage stochastic programming scheme is formulated where in the first stage the energy market is cleared according to the forecasted amount of wind generation and demands and in the second stage the real time market is solved according to the assumed scenarios. Both stages are formulated as mixed integer linear programming problems. The introduced stochastic day-ahead scheduling scheme also regards load forecasting errors and unit/line outages. In order to generate sufficient scenarios for wind generation and demand uncertainties, the scenario generation procedure is defined as an optimization problem which is solved by particle swarm optimization algorithm.

A Solution to the Chance-Constrained Two-Stage Stochastic Program for Unit Commitment With Wind Energy Integration

This paper presents a unit commitment problem with uncertain loads and wind power. A chance-constrained two-stage stochastic program programming formulation is proposed for the stochastic day-ahead scheduling. The loss of load probability (LOLP) and the probability of wind power utilization less than certain level, referred to as loss of wind probability (LOWP), and transmission line overloading probability (TLOP) are modeled as chance constraints, to provide high reliability of power supply and to ensure the high utilization of wind generation. The chance-constrained stochastic programming formulation is converted into an equivalent deterministic formulation by a sequence of approximations and verification. Correlations between loads at different buses and correlations between wind power at different buses are also considered by the proposed approach. Numerical tests are performed on a six-bus system and a modified IEEE 118-bus system. Results validate the viability of the proposed algorithm formulation for the day-ahead stochastic scheduling. The impact of different levels of LOLP and LOWP on the required up/down spinning reserves are also presented in this paper.

Electricity-Natural Gas Operation Planning With Hourly Demand Response for Deployment of Flexible Ramp

This paper proposes an integrated stochastic day-ahead scheduling model to dispatch hourly generation and load resources and deploy flexible ramping for managing the variability of renewable energy system. A comprehensive framework for the natural gas transportation network is considered to address the dispatchability of a fleet of fuel-constrained natural gas-fired units. System uncertainties include the day-ahead load and renewable generation forecast errors. Illustrative examples demonstrate that the real-time natural gas delivery can directly impact the hourly dispatch, flexible ramp deployment, and power system operation cost. Meanwhile, the demand side participation can mitigate the dependency of electricity on natural gas by providing a viable option for flexible ramp when the natural gas system is constrained.

Hourly Electricity Demand Response in the Stochastic Day-Ahead Scheduling of Coordinated Electricity and Natural Gas Networks

This paper studies the role of hourly economic demand response in the optimization of the stochastic day-ahead scheduling of electric power systems with natural gas transmission constraints. The proposed coordinated stochastic model (referred to as EGTran) considers random outages of generating units and transmission lines, and random errors in forecasting the day-ahead hourly loads. The Monte Carlo simulation is applied to create multiple scenarios for representing the coordinated system uncertainties. The nonlinear natural gas network constraints are linearized and incorporated into the stochastic model. Numerical results demonstrate the benefits of applying the hourly economic demand response for enhancing the scheduling coordination of natural gas and electricity networks. It is demonstrated that electricity demand response would offer a less volatile hourly load profile and locational marginal prices, and less dependence on natural gas constraints for the optimal operation of electric power systems. The proposed model for EGTran could be applied by grid operators for the hourly commitment and dispatch of power system units.

Thermal Generation Flexibility With Ramping Costs and Hourly Demand Response in Stochastic Security-Constrained Scheduling of Variable Energy Sources

This paper proposes a stochastic day-ahead scheduling of electric power systems with flexible resources for managing the variability of renewable energy sources (RES). The flexible resources include thermal units with up/down ramping capability, energy storage, and hourly demand response (DR). The Monte Carlo simulation (MCS) is used in this paper for simulating random outages of generation units and transmission lines as well as representing hourly forecast errors of loads and RES. Numerical tests are conducted for a 6-bus system and a modified IEEE 118-bus system and the results demonstrate the benefits of applying demand response as a viable option for managing the RES variability in the least-cost stochastic power system operations.

Demand Response Exchange in the Stochastic Day-Ahead Scheduling With Variable Renewable Generation

This paper proposes a pool-based demand response exchange (DRX) model in which economic demand response (DR) is traded among DR participants as an alternative for managing the variability of renewable energy sources (RES). Load curtailment bids are provided by individual DRX participants and the DRX is cleared by maximizing the total social welfare, which is subject to supply-demand balance and individual bidders’ inter-temporal operation constraints. The proposed DRX model is further integrated in the current context of the ISO’s day-ahead scheduling in electricity markets. A two-step sequential market clearing framework is presented in which the ISO’s stochastic day-ahead scheduling is simulated first for calculating the expected locational marginal prices (LMPs) and then, the proposed DRX is cleared successively using the expected LMPs. The simulation of the ISO’s stochastic day-ahead scheduling incorporates random outages of system components and forecast errors for hourly renewable generation and loads. The decomposition-based method is employed to solve the ISO’s day-ahead scheduling in the base case and scenarios. Numerical tests are performed for a 6-bus system and an IEEE 118-bus system. The results demonstrate the benefit of utilizing the DRX model for customer market participation in the ISO’s day-ahead market scheduling.

Coordination of Interdependent Natural Gas and Electricity Infrastructures for Firming the Variability of Wind Energy in Stochastic Day-Ahead Scheduling

In this paper, the coordination of constrained electricity and natural gas infrastructures is considered for firming the variability of wind energy in electric power systems. The stochastic security-constrained unit commitment is applied for minimizing the expected operation cost in the day-ahead scheduling of power grid. The low cost and sustainable wind energy could substitute natural gas-fired units, which are constrained by fuel availability and emission. Also, the flexibility and quick ramping capability of natural gas units could firm the variability of wind energy. The electricity and natural gas network constraints are considered in the proposed model (referred to as EGTran) and Benders decomposition is adopted to check the natural gas network feasibility. The autoregressive moving average (ARMA) time-series model is used to simulate wind speed forecast errors in multiple Monte Carlo scenarios. Illustrative examples demonstrate the effectiveness of EGTran for firming the variable wind energy by coordinating the constrained electricity and natural gas delivery systems.

Hourly demand response in day‐ahead scheduling for managing the variability of renewable energy

This study proposes a stochastic optimisation model for the day-ahead scheduling in power systems, which incorporates the hourly demand response (DR) for managing the variability of renewable energy sources (RES). DR considers physical and operating constraints of the hourly demand for economic and reliability responses. The proposed stochastic day-ahead scheduling algorithm considers random outages of system components and forecast errors for hourly loads and RES. The Monte Carlo simulation is applied to create stochastic security-constrained unit commitment (SCUC) scenarios for the day-ahead scheduling. A general-purpose mixed-integer linear problem software is employed to solve the stochastic SCUC problem. The numerical results demonstrate the benefits of applying DR to the proposed day-ahead scheduling with variable RES.