Bi-level Dispatch Research Articles

Day-ahead dispatch of electricity-hydrogen systems under solid-state transportation mode of hydrogen energy via FV-IGDT approach

Hydrogen energy has the advantages of being clean and carbon-free, convenient storage, and easy conversion to electrical energy. The use of renewable energy electrolysis for hydrogen production is an important solution to promote clean energy utilization and decarburization in the power and transportation industries. However, hydrogen has a low hydrogen storage density and is prone to explosion. The high cost and risk of hydrogen transportation pose challenges to the promotion and utilization of hydrogen. To address these difficulties, this paper proposes a day-ahead dispatch of electricity-hydrogen systems (EHS) under the solid-state transportation mode of hydrogen energy. Firstly, based on the Van't Hoff equation, the relationship between gas pressure and the reaction temperature is established during the gas-solid conversion process, and a hydrogen energy solid-state transport model based on a magnesium-based hydrogen transport vehicle (MHTV) is proposed. Secondly, a renewable energy uncertainty set is established based on the information-gap decision theory (IGDT) envelope constraint for addressing the uncertainty of renewable energy and a day-ahead bi-level dispatch model of the EHS is constructed based on the solid-state transportation mode of hydrogen energy. Finally, the IGDT approach considering fuzzy variables (FV-IGDT) is presented by using a fuzzy variable membership function, and the proposed bi-level model is transformed into a single-level model for a solution based on this approach. The effectiveness of the proposed model and solution method is verified through simulation on an EHS consisting of a modified IEEE-118 power system, 2 hydrogen production stations (HPS), and 10 hydrogen refueling stations (HRS).

A bi-level dispatch optimization of multi-microgrid considering green electricity consumption willingness under renewable portfolio standard policy

Under the policy of renewable portfolio standard (RPS), many microgrid (MG) users with high electricity consumption are installed with renewable energy (RE) generation devices to complete their RE consumption responsibility and reduce carbon emissions. In view of the conflict between the goals of microgrid operators (MGO) and MG users, it is a challenge to achieve economic optimization for both MGO and MG users at the same time when they participate in system dispatch and complete RE consumption responsibility. Therefore, this paper proposes a bi-level noncooperative dispatch strategy that takes into account user satisfaction and RE consumption willingness. In this bi-level model, the upper level is led by an MGO who decides electricity retail prices, while the lower level is followed by MG users who participate in demand response (DR) and change electricity purchase strategies. First, a novel comprehensive evaluation model is formulated to evaluate users' willingness to consume green electricity. Based on this, users' consumption behavior of green and thermal electricity is modeled independently not only in electricity trading but also in power dispatching. Second, optimization objectives are established with the goal of maximizing MG users' welfare, including user satisfaction improvement and energy purchase cost reduction, as well as maximizing the revenue of the MGO. Then, a Stackelberg game model is constructed, and a differential evolution algorithm nested CPLEX is used to solve the Stackelberg game problem of balancing the interests of both parties. Finally, our simulation analysis shows that under the dispatch strategy in this paper, load peak-valley difference is reduced. In addition, when the green electricity generated within the system is sufficient, based on a high level of users' willingness to consume green electricity, the total consumption of green electricity is increased by 29.5% and the overall welfare of users is increased by 0.75%.

Robust Bilevel Optimal Dispatch of Park Integrated Energy System Considering Renewable Energy Uncertainty

This paper focuses on optimizing the park integrated energy system (PIES) operation, and a robust bilevel optimal dispatch is proposed. Firstly, the robust uncertainty set is constructed based on the K-means++ algorithm to solve the uncertainty of renewable energy sources output in PIES. Then, the bi-level dispatch model is proposed, with the operator as the leader and consumers as the follower. The upper model establishes an electricity-heat-gas integrated energy network, and the lower model considers the demand response of consumers. Optimizing the pricing strategies of energy sources to determine the output of each energy conversion equipment and the demand response plan. Moreover, analyzing the decision-making process of the robust bi-level model and the solution method is given. Finally, case studies show that the proposed dispatch model can increase operator profits and reduce consumers’ energy costs. The in-sample and out-of-sample simulations demonstrate that the proposed ellipsoid uncertainty set possesses high compactness, good robustness, and low conservatism.

Predictive Analytics for Enterprise Innovation of Retail Energy Market Modeling of Integrated Demand Response and Energy Hubs

Many combined heat and power (CHP) energy hubs work within the following heat load mode in the wintertime to supply the request for heat, and renewable energy has been often restricted in the unified energy network (UEN) markets. The power Internet of Things (PIoTs) has enabled UEN to transmit data increasingly frequently. As a result of flexible connections among various UEN networks, renewable energy increases its accommodation capacity considerably. Thus, the purpose of the study is to optimize UEN within the backdrop of PIoTs. According to the impact of PIoTs on UEN, this paper develops the combined demand response (DR) process and the layout of the important parts of UEN. Afterward, this study develops a bi-level economic dispatching process based on the cyber-physical systems of PIoTs and UEN. In the dispatching process, the higher level optimizes the total UEN function; the lower level optimizes the demand-side equipment output and combined DR. Then, the gray wolf optimization scheme is used to solve the bi-level dispatch. Lastly, the standard UEN and the practical network have been used to verify the efficiency of the suggested process.

Optimized Particle Swarm Algorithm for Advanced Bi-Level Dispatch in New Energy Power Systems

Optimized Particle Swarm Algorithm for Advanced Bi-Level Dispatch in New Energy Power Systems

The Impact of Wind Generation on Operating Cost and Profit in a Bilevel Optimization Dispatch Model

In this paper, the problem of economic dispatch of energy of an electrical system is solved, based on three different approaches: first is deterministic, a second is stochastic and a third given by bilevel programming. It is observed that when solving the deterministic economic energy dispatch, results follow the principle of order of merit, likewise under some scenarios availability of a large flexible capacity during the power balance stage becomes necessary. In another hand, the stochastic model becomes a tool that optimizes flexible capacity by sacrificing the order of merit which is not the case of bilevel model that adopts both, order of merit and optimization of flexible capacity by choosing an optimum of wind power to dispatch. In terms of profit, it has been observed that under the stochastic approach those flexible participants must fall into losses under certain scenarios. However, under the bilevel approach, the aforementioned issue is improved. Finally and in order to illustrate the impact of the bilevel approach a 24-node system serves as a test system to obtain results and demonstrate the economic advantages of an bilevel dispatch.

Bi-level dispatch and control strategy based on model predictive control for community integrated energy system considering dynamic response performance

Integrated energy systems have recently attracted interest for the purpose of energy development and utilization owing to the increase in environmental pollution and the shortage of fossil energy. However, the integration of a high proportion of renewable energies and controllable loads in the system increases the uncertainty of system operation significantly. In order to rapidly track the system fluctuations and accurately control the operating equipment, a bi-level and multi-timescale dispatch and control strategy based on model predictive control is proposed for a community integrated energy system (CIES) considering dynamic response performance. The upper-level optimizer completes the rolling forecast of renewable outputs and loads over a long-time horizon, and builds an economic rolling optimal scheduling model with consideration of feedback correction. The lower-level controller establishes a closed-loop dynamic performance optimization control model over a short time scale by real-time controlling the system’s upstream energy equipment. The simulation results on a modified CIES located in Beijing, China demonstrate that the proposed bi-level strategy increases the prediction accuracy while ensuring the economic efficiency of the system operation, and improves the dynamic tracking ability of the equipment and the overall energy supply dynamic response rate of the system, which provides a fundamental way for efficient application of CIES.

Bi-Level Dispatch and Control Architecture for Power System in China Based on Grid-Friendly Virtual Power Plant

Non-synchronous renewable energy sources (RESs) have strong volatility and low inertia, which brings about great challenges on the accommodation of RESs and the security and stability of power systems. This paper proposes a bi-level power system dispatch and control architecture based on the grid-friendly virtual power plant (GVPP), so as to accommodate RESs flexibly and securely. The typical dispatch and control system of the power system in China is presented, and the particular challenges stemming from non-synchronous RESs are analyzed. The functional requirements, concept, and fundamental design of the GVPP are provided, which is distinguished from traditional virtual power plants (VPPs) for its active participation in power system stability control. Based on the cloud platform, a bi-level dispatch and control architecture considering two objectives is established. First, in the inner level, the GVPP operates to promote the accommodation of RESs under normal condition. Then, from the perspective of out-level power systems, GVPPs serve as spinning reserves for power support under contingencies. Besides, the key problems to be solved in the development of the GVPP-based architecture are summarized. Although the architecture is proposed for the power system in China, it can be applied to any power systems with similar challenges.

Bi-Level Load Peak Shifting and Valley Filling Dispatch Model of Distribution Systems With Virtual Power Plants

Distributed energy resources (DERs) have been widely involved in the optimal dispatch of distribution systems which benefit from the characteristics of reliability, economy, flexibility, and environmental protection. And distribution systems are gradually transforming from passive networks to active distribution networks. However, it is difficult to manage DERs effectively because of their wide distribution, intermittency, and randomness. Virtual power plants (VPPs) can not only coordinate the contradiction between distribution systems and DERs but also consider the profits of DERs, which can realize the optimal dispatch of distribution systems effectively. In this paper, a bi-level dispatch model based on VPPs is proposed for load peak shaving and valley filling in distribution systems. The VPPs consist of distributed generations, energy storage devices, and demand response resources. The objective of the upper-level model is smoothing load curve, and the objective of the lower-level model is maximizing the profits of VPPs. Meanwhile, we consider the quadratic cost function to quantify the deviation between the actual output and the planned output of DGs. The effectiveness of the bi-level dispatch model in load shifting and valley filling is proved by various scenarios. In addition, the flexibility of the model in participating in distribution system dispatch is also verified.

Economic-environmental equilibrium-based bi-level dispatch strategy towards integrated electricity and natural gas systems

Even though economic dispatch of integrated electricity and natural gas systems using optimization techniques has been widely conducted, a cooperative distributed and multi-objective optimization is still needed for meeting multi-energy demands from both conventional and renewable energy resources. In this study, an original bi-level economic-environmental equilibrium model is proposed to optimize a dispatch strategy for an integrated system, which include combined cooling, heating, and power and power to gas technologies. By optimizing cooperative distributed decisions, the system attempts to provide heat/cooling, power and natural gas loads. An equilibrium consideration of economic energy supply and environmental carbon emission control is achieved using a multi-objective optimization in the model. To reflect interactions of individual system operators in a cooperative distributed manner, a bi-level optimization is applied to address the respective decisions and objective functions. Subsequently, an interactive solution approach based on Non-dominated Sorting Genetic Algorithm-II and genetic algorithm is specifically designed to solve the model by updating individual decisions interactively and iteratively, and the optimality of an integrated solution lies in the capability to coordinate two system operators for a reliable, economic and environmental energy supply. Performance of the dispatch strategy is validated for a case with various system configurations. The obtained results showed that multi-energy needs are met, economic-environmental trade-offs achieved and hierarchical interactions addressed. Proper managerial suggestions are also provided based on comparison analyses. The proposed method is therefore worthy of being popularized in integrated systems.

Hierarchical optimal scheduling method of heat-electricity integrated energy system based on Power Internet of Things

To guarantee the heat demand during winter, most combined heat and power (CHP) units in the integrated energy system (IES) usually work under following heat load (FTL) mode, and the renewable energy accommodation is limited. With the development of Power Internet of Things (PIoT), the information exchange in IES become more frequent. Through flexible interaction between different networks in IES, the accommodation capacity of renewable energy can increase significantly. Therefore, this paper focus on the optimization of IES under the background of PIoT. Firstly, based on the influence of PIoT on IES, a novel integrated demand response (DR) way and the model of the critical components in IES are established. Secondly, a Bi-level economic dispatching method for regional IES is developed, considering the cyber-physical infrastructure of PIoT and IES. The upper level of the dispatching method is used to optimize the overall IES operation; the lower level is to optimize the output of demand-side facilities and integrated DR. Thirdly, with adaptive particle swarm optimization (APSO) algorithm, the solution method for the Bi-level dispatch is established. Finally, the feasibility and effectiveness of the proposed method are verified in a standard IES and a real system in northern China.

A low‐carbon dispatch of power system incorporating active distribution networks based on locational marginal emission

Involvement of active distribution networks (ADNs) in emission management can give full play to the carbon‐reduction potential of distributed generations (DGs) and demand response. With the development of smart grid, low‐carbon coordinated scheduling of power system with ADNs will become a new trend. A bilevel dispatch model based on locational marginal emission (LME) is proposed to achieve carbon reduction from both generation and load sides. The main grid dispatch is a problem of dynamic optimal power flow aiming at reducing both the operating and emission costs, in which LME under AC condition is adopted to calculate the carbon emission produced by node power. The ADN level is the DG generation schedule with the objective of minimizing both the cost and system emission based on LME. The main grid and ADNs interact and cooperate with each other by locational marginal price (LMP) and LME to cut the carbon pollution. Simulation results in a modified IEEE 30‐bus system and an IEEE 118‐bus system show that compared with the traditional LMP‐based coordinated scheduling, the bilevel dispatch model based on LME can reduce carbon emission effectively and promote ADNs' participation in system carbon emission management. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.