Dynamic Dispatch Research Articles

Distributed online learning and dynamic robust standby dispatch for networked microgrids

Appropriate distributed dynamic standby dispatch schemes are of great importance to manage distributed energy systems and integrate large-scale renewables without the guidance of central intelligence or day-ahead forecasting. This study focuses on systems based on networked microgrids (MGs), which include self-contained medium-voltage MGs—consisting of renewable or dispatchable generators, energy storage systems (ESS), and flexible loads. An online dynamic dispatch scheme is proposed to support autonomous operation or to serve as a standby dispatch scheme in an emergency when a dispatch center is unavailable or day-ahead planning is infeasible. First, the multi-MG energy management problem is modeled into several distributed coupled subproblems. Afterwards, taking Lyapunov drift and online learning of future costs into consideration, the distributed problem is transformed into a robust long-term optimization. Moreover, a Lyapunov-based dynamic algorithm is employed to solve the problem in a fully distributed fashion. Finally, taking an actual system as an example, the superiority and feasibility of the proposed strategy are verified by simulation. The proposed distributed online standby dispatch has good performance in the long-term economy optimization and queue stability without any day-ahead forecasting and central decision-making compared with existing frameworks.

Dynamic Optimal Dispatch of Energy Systems with Intermittent Renewables and Damage Model

With the increasing penetration of intermittent renewable energy sources into the grid, there is a growing need for process systems-based strategies that integrate dispatchable and variable energy systems for supplying the demand while maintaining grid reliability. The proposed framework corresponds to a dynamic mixed-integer linear programming optimization approach that integrates coal-fired and natural gas-fired power plants, NaS batteries for energy storage, and solar/wind energy to supply the demand. This optimization approach considers an economic goal and constraints to provide power balance while maintaining the overall damage of the natural gas combined cycle (NGCC) power plant drum under a maximum stress as well as avoiding the overheating of the NGCC superheater and reheater. Renewable curtailment levels are also retained at minimum levels. Case studies are analyzed considering different loads and renewable penetration levels. The results show that the demand was met for all cases. Grid flexibility was mostly provided by the NGCC, while the batteries were used sparingly. In addition, considering a CO2 equivalent analysis, the environmental performance was intrinsically connected to grid flexibility and the level of renewable penetration. Stress analysis results reinforced the necessity for an equipment health-related constraint.

A fully decentralized dual consensus method for carbon trading power dispatch with wind power

The global-based and partition-based dynamic power dispatch problems with wind power integrated into the carbon emission trading system are established and investigated. To meet this challenge, a distributed dual consensus algorithm based the alternating direction method of multipliers is implemented by sharing Lagrangian multipliers associated with coupling constraints between partitioned subproblems rather than phase angles on adjacent buses that are usually shared, thus protecting the key private information of each subsystem. Furthermore, a fully decentralized algorithm is proposed by adopting the finite-time average consensus algorithm, which enables each partition to iteratively approach a consensus of its shared information in a finite number of steps. For comparison purposes, a global-based centralized optimization is implemented at first, adopting the effect of carbon price on the operation of a modified IEEE-30 bus system, followed by tests of the proposed algorithms with three different partitioning methods of power systems. Results illustrate that a higher carbon price can be regarded as an incentive to decrease the wind curtailment rates and spur the increased use of clean fuel. Compared with the results of the centralized optimization, both the algorithms can achieve satisfactory convergence accuracies, although the fully decentralized algorithm requires slightly longer time for computation.

An integrated solution for optimal generation operation efficiency through dynamic economic dispatch

We propose a total and intelligent solution for optimal demand management for energy efficiency under the new framework of true dynamic economic dispatch (DED) which integrates advanced metering, new modelling for demand, dynamic load dispatch and bidding strategy together. It involves the following essential modules. (i) Dynamic economic dispatch is a true dynamic optimization formulation. With real time measurements and both supply and demand modelling, the optimization will produce the optimal plant operation modes and settings which give the highest efficiency of the overall system and are fed back to the plants for execution. (ii) Dynamic sensing is real time sensing and data acquisition system to access the real operating status of the generation plant with measurement of relevant variables. The big data technology is adopted for construction of the overall hardware and software to store huge volume of plant and market data and perform fast off line modelling and design as well as on line real time control. (iii) Dynamic modelling is model building to forecast energy demands and generation over time. Under the DED, these modules are well integrated to work together and achieve an optimal demand management for maximum energy efficiency. Its solution, verification and field implementation are reported.

Power Dispatching Considering Copula Correlation of Multiple Wind Farms Generation

The dynamic economic dispatching of power system connected with multiple wind farms is a typical stochastic programming problem. How to model the randomness of wind power and how to solve this complex stochastic optimization problem are the key points. In this paper, copula theory is used to formulate the correlation of multi-wind farms generation. Then, the dynamic economic dispatching model is founded with the fuel consumption, gas pollution emission fees and purchase costs as the optimized objective. The two-stage compensation algorithm is then introduced to solve the dispatching problem. In this algorithm, the conventional (nonstochastic) decision variables and stochastic variables are decoupled, which separate the dynamic dispatching model into two stage modes. The optimal dispatching result is worked out by iteration between the two stage models. Case studies on IEEE118-bus system shows that the proposed algorithm can drastically reduce computational burden, and satisfy the actual requirements of engineering practice.

Artificial Learning Dispatch Planning for Flexible Renewable-Energy Systems

Environmental and economic needs drive the increased penetration of intermittent renewable energy in electricity grids, enhancing uncertainty in the prediction of market conditions and network constraints. Thereafter, the importance of energy systems with flexible dispatch is reinforced, ensuring energy storage as an essential asset for these systems to be able to balance production and demand. In order to do so, such systems should participate in wholesale energy markets, enabling competition among all players, including conventional power plants. Consequently, an effective dispatch schedule considering market and resource uncertainties is crucial. In this context, an innovative dispatch optimization strategy for schedule planning of renewable systems with storage is presented. Based on an optimization algorithm combined with a machine-learning approach, the proposed method develops a financial optimal schedule with the incorporation of uncertainty information. Simulations performed with a concentrated solar power plant model following the proposed optimization strategy demonstrate promising financial improvement with a dynamic and intuitive dispatch planning method (up to 4% of improvement in comparison to an approach that does not consider uncertainties), emphasizing the importance of uncertainty treatment on the enhanced quality of renewable systems scheduling.

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.

A deep learning approach for the dynamic dispatching of unreliable machines in re-entrant production systems

This research combines deep neural network (DNN) and Markov decision processes (MDP) for the dynamic dispatching of re-entrant production systems. In re-entrant production systems, jobs enter the same workstation multiple times and dynamic dispatching oftentimes aims to dynamically assign different priorities to various job groups to minimise weighted cycle time or maximise throughput. MDP is an effective tool for dynamic production control, but it suffers from two major challenges in dynamic control problems. First, the curse of dimensionality limits the computational performance of solving large MDP problems. Second, a different model should be built and solved after system configuration is changed. DNN is used to overcome both challenges by learning directly from optimal dispatching policies generated by MDP. Results suggest that a properly trained DNN model can instantly generate near-optimal dynamic control policies for large problems. The quality of the DNN solution is compared with the optimal dynamic control policies through the standard K-fold cross-validation test and discrete event simulation. On average, the performance of the DNN policy is within 2% of optimal in both tests. The proposed artificial intelligence algorithm illustrates the potential of machine learning methods in manufacturing applications.

Experimental dynamic dispatch of a 60 kW proton exchange membrane electrolyzer in power-to-gas application

A 60 kW PEM electrolyzer was modified to have dynamic dispatch capabilities through the use of an external mass flow controller and was subsequently operated and studied in detail as a part of the UC Irvine power-to-gas (P2G) demonstration project. The system operated in load following for both rooftop solar PV output and aggregated wind farm power. The electrolyzer system was able to handle 100% up and down ramps of the electrolyzer stack in sub-second intervals during variable renewable energy load following experiments. Overall system efficiency was improved by imposing a minimum load condition to avoid high parasitic losses at low power conditions, and cycling the system off/on as required and enabled by quick cold start-up capability. Performance was characterized for varying levels of part load and dynamic operation. The gas drying process was found to be a significant source of loss at low power conditions.

Dynamic dispatching and repositioning policies for fast-response service networks

We address the problem of dispatching and pro-actively repositioning service resources in service networks such that fast responses to service requests are realized in a cost-efficient way. By formulating this problem as a Markov decision process, we are able to investigate the structure of the optimal policy in the application domain of service logistics. Using these insights, we then propose scalable dynamic heuristics for both the dispatching and repositioning sub-problem, based on the minimum weighted bipartite matching problem and the maximum expected covering location problem, respectively. The dynamic dispatching heuristic takes into account real-time information about both the state of equipment and the fleet of service engineers, while the dynamic repositioning heuristic maximizes the expected weighted coverage of future service requests. In a test bed with a small network, we show that our most advanced heuristic performs well with an average optimality gap of 4.3% for symmetric instances and 5.8% for asymmetric instances. To show the practical value of our proposed heuristics, extensive numerical experiments are conducted on a large test bed with service logistics networks of real-life size where significant savings of up to 56% compared to a state-of-the-art benchmark policy are attained.

Stochastic dispatch of energy storage in microgrids: An augmented reinforcement learning approach

The dynamic dispatch (DD) of battery energy storage systems (BESSs) in microgrids integrated with volatile energy resources is essentially a multiperiod stochastic optimization problem (MSOP). Because the life span of a BESS is significantly affected by its charging and discharging behaviors, its lifecycle degradation costs should be incorporated into the DD model of BESSs, which makes it non-convex. In general, this MSOP is intractable. To solve this problem, we propose a reinforcement learning (RL) solution augmented with Monte-Carlo tree search (MCTS) and domain knowledge expressed as dispatching rules. In this solution, the Q-learning with function approximation is employed as the basic learning architecture that allows multistep bootstrapping and continuous policy learning. To improve the computation efficiency of randomized multistep simulations, we employed the MCTS to estimate the expected maximum action values. Moreover, we embedded a few dispatching rules in RL as probabilistic logics to reduce infeasible action explorations, which can improve the quality of the data-driven solution. Numerical test results show the proposed algorithm outperforms other baseline RL algorithms in all cases tested.

An Effective Solution to Single-Area Dynamic Dispatch Using Improved Chimp Optimizer

This paper proposes the Improved Chimp Algorithm (ICHIMP) to solve single area dynamic economic load dispatch (ELD) problem of electric power system. Chimp is a biologically-stimulated heuristic optimization technique, which is embedded on impersonating the technique chimps hunt for food and remain existent by escaping from their adversary. The particularity of ICHIMP is that the chimps move in group for hunting but each chimp searches the prey independently. The single area dynamic dispatch problem is described as non-linear, complex and forced optimization problem with objective function to curtail the total generation price, whereas fulfilling the correspondence and dissimilarity constraints of the system. This proposed algorithm has been tested on five different test systems consisting of 3, 6, 13, 20 and 40- generating units.. The test results of ICHIMP determine its superiority over other existing algorithms addressed in literature and show that it outperforms for Single area dynamic dispatch problem of electric power system.

Multi-Objective Dynamic Economic Emission Dispatch of Microgrid Using Novel Efficient Demand Response And Zero Energy Balance Approach

Energy management system (EMS) in microgrids (MGs) encompasses both dynamic economic dispatch (DED) and dynamic emission dispatch problems, known as dynamic economic emission dispatch (DEED). In this paper, this system is implemented for an MG with different distributed generations. The aim of 24-hour-cycle scheduling is to determine the generation level of diesel generators (DGs), participation rate of responsive loads and their incentives, charging/discharging status of the battery, and the amount of exchanged energy with the main grid. The scheduling task is performed with the objectives of minimizing operation costs and emissions and maximizing the benefit of MG operator (MGO) resulting from the demand response (DR). Due to their interactive operation, the customers’ behavioral model and the relevant cost functions have a significant impact on the optimal operation. In this paper, the cost function of customers is developed in the incentive-based DR program (DRP) with the aim of receiving a more realistic incentive and then it is combined with DEED. Finally, the zero energy balance (ZEB) interaction between the MG and the main grid is proposed as a solution. Regarding the multi-objective and nonlinear optimization problem, the proposed method, with the help of whale optimization algorithm (WOA), is implemented on a small-scale MG in MATLAB software, where electrical loads are also taken into account. Simulation results highlight the fact that the integration of DR into DEED along with import/export ZEB between the MG and the main grid leads to optimal operation and reduced power imported from the main grid, and helps maintain the load/generation balance.

Multi-point Analysis of Economic, Environmental, Static and Dynamic Dispatching of an Energy Mix in the Presence of STATCOM by the U-NSGA-III Genetic Algorithm

In an energy environment with multiple production sources, operators are generally confronted with the optimal choice of sources which minimizes polluting gas emissions, losses and marginal production costs while meeting the contractual requirements for maintaining voltage in the ranges required. The present work consisted of optimizing an energy mix in the presence of multi-STATCOM in an interconnected network. Indeed, the (DEE) is a concrete real time problem in electrical energy production systems. This paper shows the impact of STATCOM on static DEE (DEES) and on dynamic DEE (DEED) using the modern genetic algorithm of type U-NSGA-III, which is based on non-dominance sorting. The optimal positioning of two STATCOMs in the application network associated with dynamic dispatching has contributed to the reduction of the total production cost, toxic gas emissions, active losses and then to the improvement of the voltage profiles and the transit of power in the branches. It is observed that the combination of DEED with the optimal positioning of FACTS in an interconnected network constitutes an efficient technico-ecological means to act in the direction of reduction on the triplet consisting of (gas emissions, losses, production cost). The relevance of the results obtained compared to the real case of operating the CEB's interconnected network, justifies the performance of the algorithmic tools developed in the context of this work.

Dynamic Dispatch and Centralized Relocation of Cars in Ride-Hailing Platforms

We consider a ride-hailing platform that seeks to maximize its profit by dynamically dispatching cars to pick up customers and centrally relocating cars from one area to another. We model the ride-hailing platform as a closed stochastic processing network. Because the problem appears intractable, we resort to an approximate analysis in the heavy-traffic regime and consider the resulting Brownian control problem. This problem is simplified considerably and reduced to a lower-dimensional singular control problem called the workload formulation. We develop a novel algorithm to solve the workload problem numerically. We apply this algorithm to the workload problem derived from the New York City taxi data set. The solution helps us derive a dynamic control policy for the New York City application. In doing so, we prescribe the ride-hailing platform to first solve an offline linear program, whose optimal solution can be interpreted as the optimal static control policy. This solution helps partition the areas of the city into pools of areas. The platform only uses the information on the fraction of cars in the various pools, which reduces the state space dimension significantly, making the problem computationally tractable. When the distribution of cars among the pools is balanced, the platform follows the optimal static control policy. Otherwise, the platform intervenes to move the system to a more balanced state by either dropping demand or using a dispatch or relocation activity that is not used under the optimal static control policy. We demonstrate the effectiveness of the proposed dynamic control policy for the New York City application using a simulation study.

A Virtual Generation Ecosystem Control Strategy for Automatic Generation Control of Interconnected Microgrids

The continuous access of new energy and distributed energy, as well as the random disturbance of load power, affect the security and stability of microgrids. A virtual generation ecosystem control (VGEC) strategy is proposed in this paper, which adopts the idea of time tunnel and the principle of a new win-loss criterion to achieve a fast automatic generation control power dispatch, optimal coordinated control of microgrids. A two-layer dynamic power dispatch structure is introduced in the proposed strategy, which combines hierarchical Q-learning with consensus theory to improve the adaptability of the consistency algorithm in complex random environments. Both the IEEE standard two-area load frequency control model and the interconnected microgrids model are used in simulation for comparison and verification. The results show that, by using the VGEC strategy, the control performance of microgrids can be improved, while can reduce power generation cost, and obtain faster convergence speed and stronger robustness compared with other algorithms.

Dynamic load restoration considering interdependency between power distribution system and urban transportation system

After a major outage, mobile emergency resources (MERs) can be dispatched via the transportation system (TS) for service restoration to critical loads in the power distribution system (PDS). In this case study, the efficiency of service restoration in the PDS is associated with the traffic efficiency of the TS, and vice versa, because the PDS and TS are mutually coupled through traffic lights and MERs. This paper proposes a service restoration method considering interdependency between the PDS and TS, which is formulated as a mixed-integer linear program (MILP). The objective includes maximizing the efficiency of both PDS restoration and TS. By solving the MILP, the dynamic load restoration and MER dispatch strategies can be obtained. For the PDS, the availability of MERs, including mobile sources and repair crews, relates to their dispatch in the TS, and their relationship is formulated as mathematical models. For the TS, the dynamic traffic flow is modeled using the cell transmission model and the effect of traffic lights is considered. Case studies validate the effectiveness of the proposed method.

A Mathematical Model to Optimize Allocation Sequence in Dispatching Problem

Truck-Shovel fleet, as the most common transportation system in open-pit mines, has a significant part of mining costs, for which optimal management can lead to substantial cost reductions. Among the available dispatch mathematical models, the multi-stage approach is well suited for allocating trucks to respected shovels in a dynamic dispatching program. However, with this kind of modeling sequencing of the allocated trucks is not possible though it is important to find out the best solution so that getting the minimum accrued cost. To comply with the shortcoming of the traditional model, in this paper, a new hybrid model is developed and applied in Copper Mine of Iran, in which for each truck an allocation matrix is considered as input to the genetic algorithm implemented to determine the best solution. According to the obtained results, the optimal sequencing of the trucks can result in a significant (31%) cost reduction in a shift.

Optimization of Dynamic Dispatch for Multiarea Integrated Energy System Based on Hierarchical Learning Method

The integrated energy system (IES) with various energy demands and distributed energy resources has been a significant approach to improve the efficiency of energy utilization. Considering the uncertainties of renewable energy sources and loads, the energy dispatch optimization for multiarea IESs is studied in this paper. Different from the most current studies, not only the electrical power and heat distribution in each area is optimized, but also the coordination power dispatch between areas. A hierarchical learning method is proposed inhere to improve the operation performance of the multiarea IESs. The proposed method with data-driven way is a model-free method which has no requirement for the accurate mathematical model. With the hierarchical structure, the electrical power dispatched between areas is optimized in upper layer, together with the dispatching optimization in each area at lower layer, to decrease the operation cost for the system and power demand from the power grid. Finite horizon discrete dynamic process model is adopted to simulate the data for learning. The simulation results show the effectiveness of the optimization policy can achieve an economic and stable operation for the multiarea IES.

Dynamic dispatch of solid oxide electrolysis system for high renewable energy penetration in a microgrid

The impacts of increasing deployment of Renewable Energy Sources (RES) on existing energy infrastructure has been investigated in a microgrid, an energy system that, with its constraints, foreshadows the challenges of the evolving electricity network. The campus microgrid of the University of California, Irvine (UCI) is modeled, including an existing natural gas-fueled combined cycle power plant, electric chilling and thermal energy storage, and analyzing the microgrid response to additional Photovoltaic (PV) installations. Open area on the campus of UCI can accommodate a maximum of 15 MW of fixed PV installation, and up to 22 MW of ground-mounted 2-axis tracking PV systems, providing together 37 MW of peak renewable capacity. The microgrid response to renewable installed capacity has been evaluated for different renewable installed capacity scenarios, ranging from the current 4 MW of installed capacity up to 35 MW. The results of the UCI microgrid analysis show that the RES penetration in supplying of the campus electrical demand could increase to 19% with the installation of 35 MW of PV, but 58% of the renewable production would be temporally in excess of demand and would have to be stored or curtailed, showing that a large-scale electrical energy storage system is required. A minimum aggregated power capacity of 300 kW of Solid Oxide Electrolysis (SOE) systems (the power capacity increment) is considered in the microgrid. The aggregate capacity is sized to absorb at least 80% of the excess annual renewable power production for each renewable capacity scenario, implementing a dispatch strategy able to respond to excess solar power available in the microgrid. The dynamic dispatch results of the modular SOE systems show that for cases with a renewable installed capacity higher than 15 MW, there is large excess electricity and so a great potential for hydrogen production. Almost 550 tons per year of hydrogen were produced for 35 MW of renewable installed capacity. The feasibility of on-site hydrogen utilization such as in the local hydrogen fueling station for fuel cell electric vehicles and the direct injection of the produced hydrogen into the gas turbine fuel inlet to reduce the natural gas consumption have been analyzed.