Optimal Dispatch Model Research Articles

IGDT-based Robust Power Dispatch for Active Distribution Network Considering Demand Response

Considering increasing uncertain renewable energy sources (RES) and flexible loads in active distribution network (ADN), this study proposes a novel optimal model for robust hourly energy scheduling of ADN. Firstly, a deterministic optimal dispatching model is formulated, which aims at minimizing the total operation cost of distribution network; Secondly, the information gap decision theory (IGDT) is employed to handle uncertainties of RES generation. One of the features of the proposed model is to take into account the impact of demand response of flexible loads and energy storage system (ESS) as the effective tools to reduce unintended costs due to uncertainty of RESs. Also, the uncertainty of RESs is handled in a way that maximum tolerable uncertainty is achieved for a given worsening of total operation cost. The model is formulated as a mixed integer nonlinear optimization problem and solved in the genetic algorithm. Numerical simulation on the IEEE 33-bus system has been performed. Comparisons with two types of probabilistic techniques demonstrate the effectiveness and benefits of the proposal.

Research on operational optimization of urban multi-energy flow system considering seasonal peak shaving of natural gas

In order to alleviate the shortage of natural gas supply in winter, relevant policies have been issued to promote the construction of gas peak-shaving and storage facilities. Large-scale gas storage can transfer the supply-demand relationship of natural gas in time sequence, which has great potential in improving the economy and reliability of urban multi-energy flow system. Facing this issue, this paper proposes a mid- and long-term energy optimization method for urban multi-energy flow system that considers seasonal peak shaving of natural gas. Firstly, the energy supply and demand features of different energy subsystems are analyzed. Then, a network model of the electricity-gas-heat multi-energy flow system is established. Considering time-of-use electricity price mechanism and the seasonal fluctuations of natural gas price, a mid- and long-term energy optimization model maximizing the annual economic revenue is established. The alternative direction multiplier method with Gaussian back substitution (ADMM-GBS) algorithm is used to solve the optimal dispatch model. Finally, the proposed method is verified by employing the actual data of the demonstration zone in Yangzhong City, China. The simulation results show that the proposed method is effective.

Research on Economic Optimization of Microgrid Cluster Based on Chaos Sparrow Search Algorithm.

With the deepening of the power market reform on the retail side, it is of great significance to study the economic optimization of the microgrid cluster system. Aiming at the economics of the microgrid cluster, comprehensively considering the degradation cost of energy storage battery, the compensation cost of demand-side controllable loads dispatch, the electricity transaction cost between the microgrids, and the electricity transaction cost between the microgrid and the power distribution network of the microgrid cluster, we establish an optimal dispatch model for the microgrid cluster including wind turbines, photovoltaics, and energy storage batteries. At the same time, in view of the problem that the population diversity of the basic sparrow search algorithm decreases and it is easy to fall into local extremes in the later iterations of the basic sparrow search algorithm, a chaos sparrow search algorithm based on Bernoulli chaotic mapping, dynamic adaptive weighting, Cauchy mutation, and reverse learning is proposed, and different types of test functions are used to analyze the convergence effect of the algorithm, and the calculation effects of the sparrow algorithm, the particle swarm algorithm, the chaotic particle swarm, and the genetic algorithm are compared. The algorithm has higher convergence speed, higher accuracy, and better global optimization ability. Finally, through the calculation example, it is concluded that the benefit of the microgrid cluster is increased by nearly 20%, which verifies the effectiveness of the improvement.

Optimal Dispatch of Integrated Community Energy System Considering Energy Flexibilities in Pipelines and Buildings

The development of cogeneration, distributed generation, information and communication technology has facilitated the application of integrated community energy system (ICES). However, some ICESs may lack operational flexibility due to the strict constraints of community heating system (CHS), which reduce the energy efficiency of ICESs. To solve this problem, an optimal dispatch model of ICES considering energy flexibilities in pipelines and buildings is proposed, in which the thermal dynamics of pipelines and buildings are described by node method and equivalent thermal parameter (ETP) method. The objective of the proposed dispatch method is to minimize the daily operation cost of ICES. What’s more, renewable energy accommodation cost and power purchase cost are both analysed to verify the improvement of ICES flexibility. It is demonstrated that the proposed method can unlock the flexibility of CHS and improve the energy efficiency of ICES.

A non-cooperative game based optimal dispatch model for integrated energy systems

A non-cooperative game based optimal dispatch model for integrated energy systems

A Distributionally Robust Power Dispatch Model for Active Distribution Network

The uncertainty of distribution network operation is increasing with the integration of large-scale renewable distributed generation (DG) units. To reduce the conservativeness of traditional robust optimization (RO) solutions, a data-driven robust optimal approach, which incorporates the superiority of both stochastic and robust approaches, is employed to solve the dispatch model in this paper. Firstly, a deterministic optimal dispatching model is established with the minimum total operation cost of distribution network; secondly, a two-stage distributed robust dispatching model is constructed based on the historical data of renewable-generators output available. The first stage of the model aims at finding optimal values under the basic prediction scenario. In the second stage, the uncertain probability distribution confidence sets with norm-1 and norm-∞ constraints are integrated to find the optimal solution under the worst probability distribution. The model is solved by column-and-constraint generation (CCG) algorithm. Numerical simulation on the IEEE 33-bus system has been performed. Comparisons with the traditional stochastic and robust approaches demonstrate the effectiveness of the proposal.

A Short-Term Optimal Dispatch Model Considering Uncertain Wind Power Output

Aiming at the security and economic problems caused by wind power connected to the grid, a short-term optimal dispatch model for power system considering the uncertain wind power output is proposed. Although wind power is an important clean energy source, wind power output is characterized by volatility and randomness, which damages the system power balance and brings difficulties to dispatching. In this paper, wind power output characteristics are described by uncertain sets of wind power output. Considering the positive and negative spinning reserve constraints, a bi-level optimization model is constructed. According to the dual principle, the bi-level optimization model can be transformed into a solvable robust optimization scheduling model. Finally, the numerical simulation shows that the proposed method can realize the utilization of wind power, rationally arrange the commitment operation and output of thermal power units, which has good robustness to wind power output fluctuations to ensure the safe and economic operation of the system.

Modelling the Integration of Residential Heat Demand and Demand Response in Power Systems with High Shares of Renewables

The EU aims to become the world’s first climate-neutral continent by 2050. In order to meet this target, the integration of high shares of Renewable Energy Sources (RESs) in the energy system is of primary importance. Nevertheless, the large deployment of variable renewable sources such as wind and photovoltaic power will pose important challenges in terms of power management. For this reason, increasing the system flexibility will be crucial to ensure the security of supply in future power systems. This work investigates the flexibility potential obtainable from the diffusion of Demand Response (DR) programmes applied to residential heating for different renewables penetration and power system configuration scenarios. To that end, a bottom-up model for residential heat demand and flexible electric heating systems (heat pumps and electric water heaters) is developed and directly integrated into Dispa-SET, an existing unit commitment optimal dispatch model of the power system. The integrated model is calibrated for the case of Belgium and different simulations are performed varying the penetration and type of residential heating technologies, installed renewables capacity and capacity mix. Results show that, at country level, operational cost could be reduced up to €35 million and curtailment up to 1 TWh per year with 1 million flexible electric heating systems installed. These benefits are significantly reduced when nuclear power plants (non-flexible) are replaced by gas-fired units (flexible) and grow when more renewable capacity is added. Moreover, when the number of flexible heating systems increases, a saturation effect of the flexibility is observed.

A dynamic decision-making method for energy transaction price of CCHP microgrids considering multiple uncertainties

Combined cooling, heating and power microgrid (CCHP microgrid) system, which has the characteristics of clean production and multi-energy comprehensive supply, has attracted great attention. Through the integration of renewable energy, etc., the electricity retailer can form a load service entity (LSE) to provide services. Transactions can be made between CCHP microgrid and LSE. However, how to determine the transaction price between them is still a problem. In addition, both the CCHP microgrid and LSE contain renewable energy with uncertain output, which affects the normal operation of the system. In this paper, we propose a dynamic decision-making method for the energy transaction price considering multiple uncertainties. According to the random output of renewable energy, the uncertainty set is constructed. Furthermore, a robust optimal dispatching model is established. According to the dispatching results, the optimal transaction price is searched by using the salp swarm algorithm (SSA). It can be seen from the simulation results: 1) This price decision method can effectively reduce the operating cost of the system; 2) The LSE acts as an energy exchange center in the entire system; 3) Using robust optimization for energy dispatching can maximize the consumption of renewable energy.

Optimal valuation of wind energy projects co-located with battery storage

The electricity market in the UK has been reformed over recent years resulting in the introduction of ‘Contracts for Difference’ as an option for wind producers. They can use these contracts only if at the same time they enter into other market contracts, i.e., Power Purchase Agreements or participation in the wholesale market. Most wind producers prefer the option of holding a long-term Power Purchase Agreement with another party. However, these agreements do not always offer the best terms for a wind producer due to them selling energy at lower electricity prices than those of the wholesale market spot prices. In addition, direct participation in the wholesale market can lead to high imbalance costs for the wind producers. In this paper, we propose an optimal dispatch model solved via dynamic programming optimisation to examine these two different business cases for wind operators alongside the existence (or not) of subsidised Contracts for Difference. A case study with an onshore UK wind farm and a lithium-based battery is provided. The proposed methodology was designed flexibly, so that it can be applied to different wind farm and battery configurations, as well as energy market structures. It is found that it is not always more profitable for the wind operator to hold a Power Purchase Agreement with another party than owning a battery storage unit and trading wind energy directly into the wholesale market. The discount rate of the Power Purchase Agreements, the capital cost of the wind farm and the strike price of the Contracts for Difference are critical factors affecting the profitability of both business cases. In addition, if subsidies are totally removed from both of them, then these are no longer economically justifiable, unless significant reductions are achieved in the capital costs of wind farms and battery storage units. Lastly, we show that considering only the calendar life of the battery (and not the cycle life) leads to significant underestimation of the investment costs.

Multiobjective optimal dispatch of microgrid based on analytic hierarchy process and quantum particle swarm optimization

Owing to the rapid development of microgrids (MGs) and growing applications of renewable energy resources, multiobjective optimal dispatch of MGs need to be studied in detail. In this study, a multiobjective optimal dispatch model is developed for a standalone MG composed of wind turbines, photovoltaics, diesel engine unit, load, and battery energy storage system. The economic cost, environmental concerns, and power supply consistency are expressed via subobjectives with varying priorities. Then, the analytic hierarchy process algorithm is employed to reasonably specify the weight coefficients of the subobjectives. The quantum particle swarm optimization algorithm is thereafter employed as a solution to achieve optimal dispatch of the MG. Finally, the validity of the proposed model and solution methodology are confirmed by case studies. This study provides reference for mathematical model of multiojective optimization of MG and can be widely used in current research field.

Optimal Dispatch With Transformer Dynamic Thermal Rating in ADNs Incorporating High PV Penetration

Under the high penetration of distributed photovoltaic (PV) resources, distribution networks face great transfer capacity and voltage rise challenges, which drives the transformation to active distribution networks (ADNs). Transformers are critical and costly components that are generally hard to reinforce or replace. Dynamic thermal rating (DTR) is an effective strategy that can enhance the transfer capacity of existing transformers according to the thermal condition of the equipment. In this article, an optimal dispatch model of ADNs incorporating transformer DTR and active management strategies is proposed. The thermal ratings of transformers are determined by the hot-spot temperature and loss of life (LOL) instead of the nameplate rating. Both the operation cost and transformer LOL are cooptimized in the model to avoid considerable insulation damage while enhancing the transfer capacity. The optimization model is transformed into a mixed-integer second-order cone programming (MISOCP) that can be easily solved. Numerical results demonstrate that the proposed model can significantly promote distributed PV accommodation by the synergy of transformer DTR and active management strategies and effectively address the tradeoff between higher utilization of transfer capacity and increased transformer insulation damage.

Stochastic optimal dispatching strategy of electricity-hydrogen-gas-heat integrated energy system based on improved spectral clustering method

The couplings and interactions among the multi-energy resources in the integrated energy system (IES) significantly improve the utilization of renewables and reduce carbon emissions. Hydrogen is considered to be one of the most potential energy carriers due to its excellent characteristics. Therefore, the use of hydrogen has become a hot spot in the energy field. Most researches on power to gas (P2G) technology do not use the potential advantages of hydrogen but only analyze the coupling relationship between electricity and natural gas, so the efficiency is low. Moreover, the uncertainties of renewable energy and load bring challenges to the power system dispatching. To solve these problems, an electricity-hydrogen-gas-heat integrated energy system (EHGHS) stochastic optimal dispatching strategy based on improved spectral clustering method is presented in this paper. First, the structure of EHGHS and the energy conversion unit in the EHGHS is modeled. A two-stage P2G technology is proposed which exploits the hydrogen utilization process and the combined heat and power generation process. Then a scenario reduction method based on improved spectral clustering is presented to describe the uncertain characteristics of renewable energy and load. The curve distance and cosine similarity are developed to represent the similarity between scenarios. Finally, the effectiveness, economics, and sensitivity of the stochastic optimal dispatching model are verified by case studies.

Multi-state techno-economic model for optimal dispatch of grid connected hydrogen electrolysis systems operating under dynamic conditions

The production of hydrogen through water electrolysis is a promising pathway to decarbonize the energy sector. This paper presents a techno-economic model of electrolysis plants based on multiple states of operation: production, hot standby and idle. The model enables the calculation of the optimal hourly dispatch of electrolyzers to produce hydrogen for different end uses. This model has been tested with real data from an existing installation and compared with a simpler electrolyzer model that is based on two states. The results indicate that an operational strategy that considers the multi-state model leads to a decrease in final hydrogen production costs. These reduced costs will benefit businesses, especially while electrolysis plants grow in size to accommodate further increases in demand.

Decentralized optimization operation for the multiple integrated energy systems with energy cascade utilization

Integrated energy systems (IESs) integrate and coordinate multiple types of energy resources, which feature enhanced energy efficiency in a wide area. Herein, energy cascading utilization technique is an effective way for multiple IESs to improve the energy utilization efficiency and reduce the operation cost by reutilizing the waste heat in the optimal dispatch model. In this paper, we focus on the small-sized IES which can refer to a plant. Here, an economic dispatch model is set up for multiple IESs to obtain an optimal dispatch decision with energy cascading utilization techniques. Furthermore, in order to preserve the privacy of participating plants, a decentralized optimization framework is proposed to solve the optimal dispatch model using an alternating direction multiplier method (ADMM), where only a limited set of information is shared among participants while convergence is guaranteed. Numerical results verify the effectiveness of the proposed model and its solution method.

Model validation and economic dispatch of a dual axis pv tracking system connected to energy storage with grid connection: A case of a healthcare institution in South Africa

Healthcare institutions, ranks the second highest energy intensive buildings in the commercial sector, with daily energy consumption ranging from 43 to 92 kWh per bed. Energy management in this sector may prove difficult due to the demand's critical and non-deferrable nature. Therefore, in this paper, a validated optimal power dispatch model of a proposed hybrid energy system connected to a healthcare institution is developed. The healthcare institution considered in this study, which will remain anonymous,11The healthcare institution considered in this paper is located in the Bloemfontein area and referred to as an anonymous healthcare entity in order to maintain confidentiality and privacy of sensitive data used in this study. is subjected to Time-of-Use (ToU) tariffs and maximum demand charges and is considering the implementation of renewable energy technologies to reduce energy costs. Consequently, given the geographical location of the building, a system consisting of a photovoltaic tracking system with a battery bank and grid connection supplying the load is proposed and modelled. The aim is to optimize the power flow between the hybrid system and battery in order to minimize the grid energy costs based on the ToU tariff while reducing the maximum demand charges from the utility. Annual potential cost savings of up to 40.9% were attained, while a projected break-even point of 7.1 years is noted with the proposed optimally controlled system as compared to the load exclusively supplied by the grid.

Coordination of Active and Reactive Power in Active Distribution Networks Based on Successive Linear Approximation

Active distribution network (ADN) is an important part of the future power market, which undertakes the mission of accommodating high penetration distributed generation. However, traditional dispatch mode of the distribution system cannot accommodate high-penetrated renewable energy. In this paper, a novel optimal scheduling model is developed to optimize the accommodation of distributed renewable energy by coordinating active and reactive power. The proposed model takes capacitor bank (CB), energy storage systems (ESS) and static var generator (SVG) as management devices, optimize the day-ahead dispatchable resources of active distribution system in a centralized manner. Different from second-order cone programming method that cannot cooperate with the maximum DG penetration objective, we apply the successive linear approximation method to transform the original non-convex and nonlinear optimal dispatch model with AC balance equation into a mixed-integer linear programming (MILP) model, so that the model can be solved efficiently and accurately by commercial optimization software. A real distribution system in Nantong is employed to verify the proposed model.

The contribution of distributed flexibility potentials to corrective transmission system operation for strongly renewable energy systems

Corrective operation of electrical transmission systems requires flexibility degrees of freedom to be reliably available when the system is in a critical state. In this study, we develop a method to quantify flexibility potentials from distributed and sector-coupling energy resources. We develop key performance indicators (KPIs) that correlate these potentials with the occurrence of critical transmission corridor loadings and, by that, quantify how often flexibility degrees of freedom are available when they are required. The method is based on a spatially and temporally resolved techno-economic dispatch optimization model and is tested on the example of Germany embedded in a central European energy system in the year 2030. In the considered strongly renewable scenario (208 GW installed renewable capacity) the supply often exceeds the demand. This leads to large-scale curtailments that can be used as a source for flexibility, particularly flexibility to re-increase generation. We find that, in total, curtailed onshore wind parks in the 110 kV-systems would have the potential to increase their feed-in power by 5 GW (upward flexibility) in the 10% most critical transmission scenarios. Central power-to-heat plants can provide 16.1 GW upward flexibility potential in these scenarios. Analyzing each transmission corridor separately, we find that corrective setpoint adjustments from the same technology combination, wind and power-to-heat, can completely compensate a sudden loss of 2 GW transmission capacity for up to 40% of critical timesteps on the transmission corridors within the given model. These findings indicate that flexibility from sector-coupling elements and decentral energy resources are a relevant resource and can be applied for corrective actions in transmission system operations.

Research on Optimal Dispatch of Water-Fire-New Energy Power System

In order to safeguard their own energy security, strengthen environmental protection and prevent climate change, many countries in the world are vigorously developing renewable energy to replace traditional fossil energy. As the largest developing country in the world, China’s energy structure and development situation are in a very serious situation, and the energy security problems are threatening the smooth implementation of the national energy strategy. Accelerating the development and utilization of renewable energy has become the best choice to solve the energy crisis in China. Wind and solar energy, as an important part of new energy, are gradually infiltrating into China’s power system. However, due to their great randomness, they are bound to bring severe challenges and a series of difficulties to the traditional power system optimal scheduling problem. China is rich in water energy resources, hydropower is the largest renewable energy that can be developed on a large scale at present, vigorously developing hydropower can significantly reduce the consumption of traditional fossil energy, and at the same time it will absorb some negative effects of new energy power generation through controllable adjustment. Therefore, it is necessary to rationally formulate the optimal dispatching strategy of multi-energy complementary power system for the development of China’s energy strategy. This paper has carried on a series of discussions and carried out the following work. (1) Comprehensively analyze the reasons for the uncertainty of new energy power generation, study the probability distribution model of prediction error, and discuss the new energy power generation scenario with the scenario analysis method. (2) The traditional combined optimal dispatching model, the combined dispatching model with new energy generation system and the multi-energy complementary power system are calculated respectively, and the optimal dispatching strategy is given through an example analysis. (3) Complex models need efficient algorithms to solve, so this paper proposes a new intelligent optimization algorithm.

Economic Optimization of Multiple Microgrids System Based on Alliance Theory

Aiming at the problem of economic operation of multiple microgrids system, a coordinated optimized operation method of multiple microgrids in integrated energy system based on alliance theory was proposed. Firstly, energy storage station was introduced into multiple microgrids optimal scheduling, the energy flow relationship between various links was studied, and an economic optimal dispatch model was established under the alliance mode of multiple microgrids and energy storage station; Secondly, constructed a Shapely value benefits distribution mechanism to carry out the initial benefits distribution of the alliance and the secondary distribution of the multiple microgrids internal benefits. Finally, taking a typical daily scenario as an example, the optimal operation scheduling strategy of multiple microgrids with the participation of energy storage station was obtained. The results showed that the method proposed in this paper can effectively improve the benefits of all participants and ensure the economic operation of multiple microgrids, at the same time, it realized the full consumption of renewable energy.