Real-time Dispatch Research Articles

Confidence Interval Based Distributionally Robust Real-Time Economic Dispatch Approach Considering Wind Power Accommodation Risk

This article proposes a confidence interval based distributionally robust real-time economic dispatch (CI-DRED) approach, which considers the risk related to accommodating wind power. In this article, only the wind power curtailment and load shedding due to wind power disturbances are evaluated in the operational risk. The proposed approach can strike a balance between the operational costs and risk even when the wind power probability distribution cannot be precisely estimated. A novel ambiguity set is developed based on the imprecise probability theory, which can be constructed based on the point-wise or family-wise confidence intervals. The worst pair of distributions in the established ambiguity set is then identified, and the original CI-DRED problem is transformed into a determined nonlinear dispatch problem accordingly. By using the sequential convex optimization method and piecewise linear approximation method, the nonlinear dispatch model is reformulated as a mixed integer linear programming problem, for which off-the-shelf solvers are available. A fast inactive constraint filtration method is also applied to further relieve the computational burden. Numerical results on the IEEE 118-bus system and a real 445-bus system verify the effectiveness and efficiency of the proposed approach.

Risk-averse real-time dispatch of integrated electricity and heat system using a modified approximate dynamic programming approach

Coordinated operation of integrated electricity and heat system can improve operation flexibility and reduce cost. However, multiple uncertainties challenge its optimal operation. This paper aims at developing a risk-averse and computationally efficient policy for real-time stochastic dispatch of integrated electricity and heat system, which improves the economy as well as avoiding the risk of high costs in critical scenarios. First, real-time dispatch of integrated electricity and heat system is formulated as a multistage risk-averse stochastic sequential optimization problem with dynamic risk measure, where combined heat and power unit, energy storage, flexible electricity and heat load are jointly utilized to minimize the risk-adjusted total costs. Next, a risk-averse dynamic programming formulation of the original problem is presented, upon which a data-driven risk-averse approximate dynamic programming is employed to address computational challenge, and develop almost optimal and computationally efficient policy. By exploiting information from training samples in off-line learning, the proposed algorithm can efficiently responses to the stochastic exogenous information. Comparative simulations with different risk-aversion preferences and different methods verify the effectiveness of the proposed algorithm.

A Linear Programming Approximation of Distributionally Robust Chance-Constrained Dispatch With Wasserstein Distance

This paper proposes a data-driven distributionally robust chance constrained real-time dispatch (DRCC-RTD) considering renewable generation forecasting errors. The proposed DRCC-RTD model minimizes the expected quadratic cost function and guarantees that the two-sided chance constraints are satisfied for any distribution in the ambiguity set. The Wasserstein-distance-based ambiguity set, which is a family of distributions centered at an empirical distribution, is employed to hedge against data perturbations. By applying the reformulation linearization technique (RLT) to relax the quadratic constraints of the worst-case costs and constructing linear reformulations of the DRCCs, the proposed DRCC-RTD model is cast into a deterministic linear programming (LP) problem, which can be solved efficiently by off-the-shelf solvers. Case studies are carried out on a 6-bus system and the IEEE 118-bus system to validate the effectiveness and efficiency of the proposed approach.

Mutual Exclusion Algorithm for Real-time Traffic Dispatching Based on Game Theory

With the rapid increase of vehicles, real-time dynamic dispatch is an important means to alleviate traffic congestion. Game theory-based real-time vehicle scheduling algorithms are often used for dynamic adjustments, but traditional algorithms only switch signal phases for fixed phases during dynamic adjustment scheduling, and cannot achieve the comprehensiveness of real-time scheduling; or phases with large differences in traffic flow when setting the duration, the reasonable waiting time for the smaller traffic phase are not considered. In order to solve the real-time dynamic adjustment of signal lights, an improved algorithm based on game theory is designed to control the signal switch in the game mode, and the waiting time of vehicles at traffic intersections can be reduced by adding parameter values of lane waiting time changes. Through experimental comparison, the algorithm reduces the vehicle stay by 3.4% and the waiting time by 4.4% compared with the fuzzy algorithm of the polling mechanism.

Two-stage stochastic scheduling model of wind-photovoltaic-storage providing flexible ramp capacity

The increasing penetration of renewable energy resources in power system has contributed to the increasing demand for operation flexibility. To address the deficiency of flexibility in wind-photovoltaic-storage hybrid system with deepening penetration of intermittent and uncertain renewable energy resources, this paper proposes emerging flexible resources including wind, photovoltaic and energy storage providing flexible ramp capacity to improve flexibility. Considering the uncertainty of load and intermittent generation, the wind-photovoltaic-storage providing flexible ramp capacity are integrated into a two-stage stochastic dispatch model including 1h day-ahead unit commitment and 15min real-time economic dispatch. To test the efficiency of the proposed model, simulation are carried out on a modified IEEE 118-bus with 54 thermal units, the results indicate that wind-photovoltaic-storage providing flexible ramp capacity can effectively alleviate the deficiency of operation flexibility, and improve the overall economic benefit.

Introduction to Laguerre‐based stochastic economic predictive functional control for optimal real‐time power dispatch under uncertainty

This study presents a Laguerre-based stochastic economic predictive functional control (L-SEPFC) method for real-time power dispatch of balance responsible parties (BRPs). In this regard, at the control layer, an L-SEPFC method operates on the energy time scale and optimises the expected economic performance of the BRP. Calculated set points in this layer are average power injection of controllable generators, as well as known disturbance of prediction wind generation. The novel L-SEPFC scheme is able to cope with the wind uncertainty occurring in the real-time operation of BRPs. Furthermore, the proposed algorithm is able to take into account the technical constraints of generators and transmission line constraints of the power network. Due to the parameterisation of the future control signal based on the Laguerre function, the proposed method is faster than the nominal predictive algorithms. Simulations on an IEEE 118-bus test system with proposed method shows improvements inability to track reference production, economic performance, imbalance energy and computational burden with respect to nominal predictive controllers.

Multi-Time Scale Optimal Dispatch for AC/DC Distribution Networks Based on a Markov Chain Dynamic Scenario Method and MPC

A multi-time scale optimal dispatch model based on the scenario method and model predictive control (MPC) in the AC/DC distribution network is established due to the uncertainty of wind and load. A Markov chain dynamic scenario method is proposed, which generates scenarios by characterizing the forecast error via empirical distribution. Considering the time correlation of the forecast error, Markov chain is adopted in the Markov chain dynamic method to simulate the uncertainty and variability in wind and load with time. A multi-time scale optimal dispatch strategy based on MPC is proposed. The operation scheduling of operation units is solved in day-ahead and intraday optimal dispatch by minimizing the expected value of total cost in each scenario. In the real-time optimal dispatch, the stability and robustness of system operation are considered. MPC is adopted in the real-time optimal dispatch, taking the intraday scheduling as reference and using the roll optimization method to compute real-time optimal dispatch scheduling to smooth the output power. The simulation results in a 50-node system with uncontrollable distributed energy demonstrate that the proposed model and strategy can effectively eliminate fluctuations in wind and load in AC/DC distribution networks.

Deep Reinforcement Learning for Economic Dispatch of Virtual Power Plant in Internet of Energy

With the high penetration of large-scale distributed renewable energy generation, the power system is facing enormous challenges in terms of the inherent uncertainty of power generation of renewable energy resources. In this regard, virtual power plants (VPPs) can play a crucial role in integrating a large number of distributed generation units (DGs) more effectively to improve the stability of the power systems. Due to the uncertainty and nonlinear characteristics of DGs, reliable economic dispatch in VPPs requires timely and reliable communication between DGs, and between the generation side and the load side. The online economic dispatch optimizes the cost of VPPs. In this article, we propose a deep reinforcement learning (DRL) algorithm for the optimal online economic dispatch strategy in VPPs. By utilizing DRL, our proposed algorithm reduced the computational complexity while also incorporating large and continuous state space due to the stochastic characteristics of distributed power generation. We further design an edge computing framework to handle the stochastic and large-state space characteristics of VPPs. The DRL-based real-time economic dispatch algorithm is executed online. We utilize real meteorological and load data to analyze and validate the performance of our proposed algorithm. The experimental results show that our proposed DRL-based algorithm can successfully learn the characteristics of DGs and industrial user demands. It can learn to choose actions to minimize the cost of VPPs. Compared with the deterministic policy gradient algorithm and DDPG, our proposed method has lower time complexity.

Risk-Based Distributionally Robust Real-Time Dispatch Considering Voltage Security

A risk-based distributionally robust real-time dispatch approach is proposed to strike a balance between the operational costs and risk as well as considering the nodal voltage security even when the distribution of uncertainties cannot be precisely estimated. To model uncertainties, a data-drive ambiguity set is developed based on the imprecise probability theory without any presumption on the probability distribution of uncertainties. The obtained ambiguity set is based on the confidence interval, which can be constructed according to actual needs by choosing the point-wise or family-wise confidence interval. Two strategies for wind farms to provide voltage support are incorporated in the proposed approach so that the nodal voltage security can be ensured. By incorporating the risk tractable estimation method and sequential convex optimization method, an efficient algorithm is developed to release the computational burden. Numerical results show that compared with the regular robust optimization, the proposed approach reduces the total operational cost to achieve statistically optimal dispatch decisions. The proposed approach also outperformances the stochastic programming in term of the operational risk reliability. Meanwhile, the nodal voltage security issues can be mitigated with the voltage support from wind farms. In conclusion, under the uncertainty of probability distributions, the proposed approach can efficiently strike a balance between the operational cost and risk while ensuring the nodal voltage security.

A framework for analyzing trade-offs in cost and emissions in power sector

Current electricity markets operate on a cost minimizing objective for power supply. However, countries across the world need to decarbonize their power systems in line with their policy objectives to mitigate climate change. In this context, this paper presents a framework to analyze synergies and trade-offs in cost and emission minimization strategies in the power sector. Emission minimizing objective can reduce emissions from existing fleets having flexibility in electricity supply, regardless of renewable energy capacity additions. This framework can also provide us with win-win strategies for reducing emissions while keeping costs low.An optimization model is developed for case study of India with data of 568 coal and 199 gas thermal units to analyze alternative real time dispatch and operating strategies. The cost and emission optimal supply strategies are compared in terms of power plant operations, emission reductions, and resulting cost of carbon abatement. The results show that 9.8% of carbon dioxide emissions can be reduced with 19% increment in the cost of electricity in emission minimizing strategy with respect to cost minimization. The operations in emission optimal supply strategy require additional 28 million dollars per day. The cost of carbon abatement is 99–129 dollars per tonne.

Probabilistic Optimal Power Flow for Day-Ahead Dispatching of Power Systems with High-Proportion Renewable Power Sources

With the increasing proportion of uncertain power sources in the power grid; such as wind and solar power sources; the probabilistic optimal power flow (POPF) is more suitable for the steady state analysis (SSA) of power systems with high proportions of renewable power sources (PSHPRPSs). Moreover; PSHPRPSs have large uncertain power generation prediction error in day-ahead dispatching; which is accommodated by real-time dispatching and automatic generation control (AGC). In summary; this paper proposes a once-iterative probabilistic optimal power flow (OIPOPF) method for the SSA of day-ahead dispatching in PSHPRPSs. To verify the feasibility of the OIPOPF model and its solution algorithm; the OIPOPF was applied to a modified Institute of Electrical and Electronic Engineers (IEEE) 39-bus test system and modified IEEE 300-bus test system. Based on a comparison between the simulation results of the OIPOPF and AC power flow models; the OIPOPF model was found to ensure the accuracy of the power flow results and simplify the power flow model. The OIPOPF was solved using the point estimate method based on Gram–Charlier expansion; and the numerical characteristics of the line power were obtained. Compared with the simulation results of the Monte Carlo method; the point estimation method based on Gram–Charlier expansion can accurately solve the proposed OIPOPF model

Flexibility Services Management Under Uncertainties for Power Distribution Systems: Stochastic Scheduling and Predictive Real-Time Dispatch

Over the last years, the role of the distribution system operator (DSO) has largely expanded. This is necessitated by the increased penetration of intermittent energy resources at the distribution level, as well as the new, more complex interactions with the transmission system operator (TSO). As such, to properly manage its system and to have an effective joint cooperation with the TSO, the DSO is required to procure and carefully manage flexibility services from distributed energy resources (DER). This paper introduces a thorough framework on optimal operational planning (day-ahead scheduling) and operational management (real-time dispatch) of active distribution systems under uncertainties, to avoid line congestions and voltage limit violations, and efficiently balance the distribution system. A two-stage stochastic programming model based on weighted scenarios is proposed to optimize the multi-period optimal power flow day-ahead scheduling, i.e., scheduled power flows at the TSO-DSO interface and reserved DER flexibility services. Subsequently, the operational management, realized with a predictive real-time dispatch model based on a constantly updated rolling horizon, aims to efficiently activate the available flexibility services to minimize deviations from the committed schedule. Different sources of flexibility are considered, with their respective response times also taken into account at real-time dispatch. The proposed framework is applied on two distribution systems and investigates the DSO’s level of risk exposure while minimizing its total cost (reservation and activation expenses). The results indicate that a less conservative approach at planning stage, despite the potential risk exposure, can lead to significant reduction in total expenses.

Selection of a Critical Time Scale of Real-Time Dispatching for Power Systems With High Proportion Renewable Power Sources

The power prediction error of uncertain renewable energy sources (URESs) affects the power balance of a power grid. In the power systems with high proportion renewable power sources (PSHPRPSs), automatic generation control (AGC) cannot accommodate the day-ahead power prediction error. The dispatching control system (DCS) of PSHPRPSs adds real-time dispatching links to modify the day-ahead dispatching plan so that the grid power error is within the range of AGC accommodation. This paper proposes a critical time scale (CTS) selection algorithm based on time aggregation characteristics for real-time dispatching of PSHPRPSs and calculates the annual CTS of real-time dispatching of power grids. The uncertainty function is used to describe the relationship between the prediction error of the URESs and the prediction lead time. The total uncertainty function is calculated based on the time aggregation characteristics and is used to select the annual CTS of real-time dispatching. The proposed algorithm quantitatively describes the relationship between the CTS and the operation proportion of URESs and also AGC accommodation capacity. The calculated annual CTS not only ensures the power balance of the power grid, but also avoids the daily change of the CTS. Using the data of URESs in the Irish power grid, the feasibility of the proposed method was verified. The research results of this paper are helpful to accommodate the day-ahead power prediction error of URESs and maintain the safe operation of power grid.

Bi-objective optimization of real-time AGC dispatch in a performance-based frequency regulation market

This paper constructs a new bi-objective optimization model of real-time automatic generation control dispatch in a performance-based frequency regulation market. It attempts to simultaneously minimize the total power deviation and the regulation mileage payment via optimally distributing the real-time total generation command to different regulation units. To handle this problem, the efficient non-dominated sorting genetic algorithm II is adopted to rapidly obtain a high-quality Pareto front for real-time AGC dispatch. A dynamic ideal point based decision making technique is designed to select the best compromise solution from the obtained Pareto front according to the minimization of total regulation variation, which can effectively avoid an excessive regulation variation for each unit. Lastly, two testing systems are used to verify the performance of the proposed technique.

Optimal Dispatch in Power Systems with Intermittent Power Sources

With the increasing integration of intermittent power sources (IPSs) into the power system, the uncertainty of IPSs requires solution and current dispatch system needs improvement. This paper aims to generate the optimal dispatch plan for day-ahead scheduling and real-time dispatch using the proposed model of characteristic optimal power flow (COPF). The integral time period represented by the median load point and the heavy and light load point with simplicity and accuracy. Simulation case studies on a 30-bus system are presented, which shows that COPF is an effective model to generate the optimal dispatch plan for power systems with high penetration of IPSs.

Marginal Bottleneck Identification in Power System Considering Correlated Wind Power Prediction Errors

This letter investigates how to identify the marginal bottleneck, which is defined as the constraint most likely to be violated with the increasing wind generation uncertainty of power system in real-time dispatch. The presented method takes the correlation of wind power prediction error (WPPE) into account, leading to an ellipsoidal formulation of wind power generation region (WGR). Based on constructed WGR, the identification procedure is formulated as a max-max-min problem, which is solved by the algorithm based on iteration linear program with the proposed method to select appropriate initial points of WPPE. Finally, two cases are tested, demonstrating the efficacy and efficiency of the procedure to identify marginal bottleneck.

Ultrashort-Term Scheduling of Interbasin Cascaded Hydropower Plants to Rapidly Balance the Load Demand

The short-term scheduling schemes of cascaded hydropower plants are based on day-ahead hydrological forecasting information. Affected by the accuracy of prediction, real-term hydrological information can considerably vary from previously forecasted values, especially for the power load and local inflows. As a result, short-term scheduling schemes are difficult to apply directly in real-time scheduling. To solve this problem, a rolling optimal hourly operation model for interbasin cascaded hydropower plants is proposed to rapidly balance the load demand. The model is deemed an ultrashort-term model, and it has a short-cycle schedule between short-term and real-time schemes. The basic strategy of solving the model is as follows. First, the day-ahead power load and local inflow information are corrected using a statistical method based on the most recent real-time information. Second, the water delay time, defined as the time between when water is released from upstream reservoirs and arrives at downstream reservoirs, is updated based on the most recent real-time information. Third, a heuristic method is proposed to dynamically update the short-term scheduling scheme over the next few hours. The objective is to minimize the maximum relative deviation between the actual water level of a reservoir at the end of 24 hours and the day-ahead forecasted value. This approach can keep the reservoir water level at the end of 24 hours close to the forecasted value and improve the accuracy of estimating the initial water level the next day. The developed method is applied to solve an ultrashort-term scheduling problem involving the cascaded hydropower plants located in Yunnan Province, China. The results indicate that the proposed model can achieve seamless coupling between short-term scheduling and real-time scheduling. The proposed method can provide a scientific basis for the real-time dispatching of large-scale hydropower plants and improve the practicality of short-term dispatching schemes.

Economic Dispatch Cost Reduction in Box-based Robust Unit Commitment

This paper proposes a box expanding method to reduce the economic dispatch (ED) cost in the box-based robust unit commitment model (BUC). As a non-anticipative robust unit commitment model for a power system under demand uncertainty, BUC co-optimizes the commitment schedule and the feasible set of the ED problem to minimize the total operating cost for the worst-case realization in a set of possible demand scenarios; the feasible set of the ED problem is modeled as a box to enable the non-anticipative real-time dispatch. Meanwhile, as BUC considers the worst-case total operating cost, the actual total operating cost may be unnecessarily high. In this paper, the box feasible set of the ED problem in BUC is expanded to a larger one via multi-objective optimization with a no-preference method. The expanded box forms a new feasible set of the ED problem, which increases the chance of reducing the actual ED cost and thus the actual total operating cost. Simulation results using 5-, 14-, and 30-bus test systems demonstrate the effectiveness and generality of the proposed method.

Stochastic economic model predictive control for real-time scheduling of balance responsible parties

This paper presents stochastic economic model predictive control (SEMPC) method for real-time economic dispatch of Balance Responsible Parties (BRPs) as load balance controller in power system structure. In this regard, SEMPC method operates on the energy time scale and optimizes the expected economic performance of the BRP. Calculated set-points are average power injection of controllable generators, as well as known disturbance of prediction wind generation. The novel SEMPC scheme is able to cope with the wind uncertainty occurring in the real-time operation of BRPs. Furthermore, the proposed algorithm can take into account the technical constraints of generators and transmission line constraints of the power network. Simulations on IEEE 9-bus and 57-bus test systems with proposed method shows improvements inability to track reference production, economic performance and imbalance energy with respect to nominal predictive controllers.

Multi-timescale coordinated schedule of interdependent electricity-natural gas systems considering electricity grid steady-state and gas network dynamics

The tight interdependency between electricity and natural gas systems brings new operation challenges to coordinate the two systems for achieving optimized multi-energy supply. The coordinated short-term schedule and real-time dispatch of an integrated electricity-natural gas system (IEGS) with energy coupling components (i.e., P2G (power to gas) assets and gas-fired generators) are proposed. Specifically, in the short-term schedule, electricity generators and gas sources are optimized in a unified model to achieve the minimal operation cost, where prevailing operation constraints related to hourly-scale steady-state power flow and minute-scale gas transmission dynamics are satisfied and extreme wind power scenarios are also considered. In the real-time dispatch, P2G assets and gas-fired generators are optimized to smooth the wind power forecast errors, aiming at mitigating impacts of wind power uncertainties on gas pressures variations. Through real-time dispatch, extreme wind power scenarios which cause violations of gas pressures will be identified and fed back to the short-term schedule problem, seeking for new operation strategies that would mitigate potential gas pressure violations induced by wind power uncertainties in real time. An IEGS, consisting of a 15-node and 14-pipeline natural gas network and a 24-bus and 35-branch power network, is established to validate the proposed approach. Simulation results demonstrate that linepack, P2G, and gas-fired generators can be utilized to effectively enhance operational economics and robustness of IEGS against uncertainties.