A Sensitivity-Based Linear Model for Optimal Dispatch of Local Flexibility

The speed of wind at the different wind generator sites fluctuates due to weather variations over time. As a result, the wind generations may fluctuate over a wide range. The fluctuations and uncertainty are presently covered, in the New Zealand electricity market (NZEM), by the reserves originally offered for the protection of larger risk units in the system and frequency regulating reserve with no cost to the wind generators. In fact, smaller units benefit from the magnitude of reserves determined by the largest risk units. The frequency of outage of wind generators and variability of wind generation outputs is quite high compared to other smaller units and these frequently need a considerable amount of reserves to cover the generation fluctuations. The wind generators, therefore, free ride on other reserve providers adding cost to the system but not necessarily sharing them in an equitable manner. This issue has not been addressed so far. We develop a dispatch optimization model that recognizes the additional risk imposed by the wind generators. The model is based on DC-OPF, driven by generator risk contingencies, security margins at the load buses to cover the large variation of load, and security margins at the wind generator buses to account for the uncertainty of wind generations. The margins are maintained using reserves. The margin required at each wind generator bus is an input to the model and can be estimated statistically from its past performance. The main thrust of this paper is to examine the effect of security margin constraints at the wind generator buses and also at the load buses on the nodal spot prices. The paper demonstrates that the generation, and demand prices at a bus are different in the presence of binding security margin constraints. Generation prices at the wind generator buses get reduced in the presence of binding security margin constraints at these buses.

Hydrogen energy plays an increasingly vital role in global energy transformation. However, existing electric–hydrogen coupled integrated energy systems (IESs) face two main challenges: achieving stable operation when integrated with large-scale networks and integrating optimal dispatching code with physical systems. This paper conducted comprehensive modeling, optimization and joint simulation verification of the above IES. Firstly, a low-carbon economic dispatching model of an electric–hydrogen coupled IES considering carbon capture power plants is established at the optimization layer. Secondly, by organizing and selecting representative data in the optimal dispatch model, an electric–hydrogen coupled IES planning model considering the integration of wind, photovoltaic (PV), diesel and storage is constructed at the physical layer. The proposed electric–hydrogen coupling model mainly consists of the following components: an alkaline electrolyzer, a high-pressure hydrogen storage tank with a compressor and a proton exchange membrane fuel cell. The IES model proposed in this paper achieved the integration of optimal dispatching mode with physical systems. The system can maintain stable control and operation despite unpredictable changes in renewable energy sources, showing strong resilience and reliability. This electric–hydrogen coupling model also can integrate with large-scale IES for stable joint operation, enhancing renewable energy utilization and absorption of PV and wind power. Co-simulation verification showed that the optimized model has achieved a 29.42% reduction in total system cost and an 83.66% decrease in carbon emissions. Meanwhile, the simulation model proved that the system’s total harmonic distortion rate is controlled below 3% in both grid-connected and islanded modes, indicating good power quality.

The electricity production from Renewable Energy (RE) in isolated locations requires long-term energy storage systems. To that end, Hybrid Energy Storage Systems (HESS), through a combination of hydrogen and batteries, can benefit from the different advantages of both technologies. This paper presents a hybrid Power-to-Power (PtP) Optimal Power Dispatch (OPD) model for isolated systems with no electric grid access. Currently, the electricity supply in such cases is usually based on a mix of RE as the primary energy source sustained by a diesel genset acting as a backup generator. In this context, the model delivers the hourly energy flows between renewable production sources, energy storage devices and the electrical load, which minimises costs and Green House Gases (GHG) emissions. For validation purposes, the model was tested through its application to a case study in an isolated area in the Canary Islands, Spain. The results show that the algorithm calculates the hourly OPD successfully for a given plant sizing, considering the defined operational states of the different assets. These operational constraints showed a decrease in the PtP round-trip efficiency of 5.4% and a reduction of the hydrogen production of 9.7%. Finally, the techno-economic analysis of the results proves that the combination of hydrogen and batteries with RE production is a feasible alternative to phasing out fossil fuels for the selected case study – reducing the diesel generator usage down to 1.2% of the yearly energy supply.

For the uncertainty of wind power, PV and load in virtual power plant (VPP), a two-stage optimal dispatching model considering the participation of demand-side response resources in VPP is developed from two stages: day-ahead and real-time, taking into account the forecast error and customer-side demand response resources. In the day-ahead stage, a day-ahead VPP optimal economic dispatch model is developed using price-based demand response and incentive-based demand response to optimize customer load curves with VPP operating cost as the target. In the real time stage, taking into account the regulation characteristics of the excitation type demand response, the real time power deviation is smoothed together with the internal generator set of VPP to realize the correction of the power output of the equipment before the day. Both phases of the model are modeled on MATLAB software and solved by the CPLEX optimization solver. The simulation results show that the introduction of price-based demand response and incentive-based demand response together can optimize the load profile of users better and reduce the operating cost of VPP; the two-stage optimal scheduling can smooth out the random fluctuations on both sides of the source load in the real-time phase and realize the economic and stable operation of VPP.

Aiming at the effective management of distributed energy resources (DERs), we propose a bi‐level optimization dispatch model which consists of a virtual power plant (VPP) and an independent system operator (ISO) in order to achieve a coordinated dispatch between the upper level objective function of minimizing system's operation cost and the lower level objective function of maximizing the VPP's net profit. However, the VPP's dispatch strategy is variable due to the threat from unavoidable uncertainties including unit outage, load forecast deviation, and renewable energy forecasting error. In order to evaluate the influences of these risks on VPP's dispatch strategy, a proper definition of expected energy not supplied (EENS) and its relationship with VPP's reserve capacity are provided. Also, chance‐constrained programming (CCP) is adopted during the analysis. By including VPP's risk cost into the dispatch model, VPP's dispatch strategy is able to keep a balance between the potential risk and economic profit. In addition, a further discussion regarding the risk cost is conducted in view of renewable energy capacity proportion and the VPP capacity proportion. Based on the analysis results of the case study provided, the proposed model serves as a foundation for VPP's economic and risk‐balanced dispatch.

When large-scale electric vehicles are connected to the grid for unordered charging, it will seriously affect the stability and security of the power system. To solve this problem, this paper proposes a regional power network optimization scheduling method considering vehicle network interaction. Initially, based on the user behavior characteristics and charging and discharging characteristics of electric vehicles, a charging and discharging behavior model of electric vehicles was established. Based on the Monte Carlo sampling algorithm, the scheduling upper and lower limits of each scheduling cycle of electric vehicles were described, and the scheduling potential of each scheduling cycle of electric vehicles was obtained. Then, the electricity price is then used as an incentive parameter to guide EV users to charge during periods of low electricity prices and participate in discharge during periods of peak electricity prices. Aiming at the highest economic efficiency, the best consumption effect of new energy and the smoothest demand-side power curve of regional power grid, a three-objective optimal dispatching model was established. In the later stage, uncertainty factors are taken into consideration by introducing the concept of interval numbers, and an interval multi-objective optimization dispatching model is established. The two dispatching models are solved by NSGA-II algorithm and improved NSGA-II algorithm, and the Pareto solution set is obtained. Finally, based on the analytic Hierarchy Process (AHP), the optimal scheduling scheme is determined. The Monte Carlo sampling method is used to simulate the user side charging demand, and the effectiveness of this method is verified. In addition, the results of the interval multi-objective optimization model and the deterministic multi-objective optimization model are compared, and it is proved that the solution results of the interval multi-objective model are more adaptive, practical and robust to the uncertain factors.

A non-linear convex cost model for economic dispatch in microgrids

This paper proposes an optimal dispatch model of an integrated energy system (IES), with carbon trading and flexible loads (FLs) considered. First, the carbon trading is introduced into the dispatch model, taking into account the carbon emissions of the entire energy supply chain. Next, flexible loads are divided into three categories, i.e. curtailable, transferable and shiftable ones, which are respectively modeled in detail. On this basis, electric network constraints, natural gas network constraints, together with internal constraints of energy hubs (EHs) are integrated into the low-carbon economic dispatch model with an objective of minimizing the operation costs including the carbon trading cost, flexible loads dispatching cost and system energy cost. Finally, an integrated energy system including 9 energy hubs is employed to demonstrate the proposed model, together with detailed analysis about the impacts of the carbon trading price and FLs on the IES.

This study proposes a day‐ahead optimal energy dispatch model for integrated energy system based on AC/DC interconnected infrastructure. In the infrastructure, micro‐turbines turn high‐frequency AC power into industrial‐frequency AC power and incorporate it into the energy system's power system. Within the process, waste heat is sent into the waste heat boiler and lithium bromide chillier to interconnect with the heating and cooling systems. DC battery and photovoltaic generations connected with the energy system's power grid through the inverter are used as the flexible energy storage and power generation unit to build an autonomous integrated energy system based on AC/DC interconnection. In the optimisation model, the goal is set as system's lowest daily operation cost and lowest energy consumption. The constraints include system operation constraints and equipment constraints. Simulation results show that by performing the schedule made by the dispatch model, the operating efficiency of various types of AC/DC equipment in the energy system has been improved, energy waste scene has been reduced significantly and energy consumption in the energy system is reduced by 16% compared with other energy supply structure without AC/DC interconnected infrastructure under traditional schedule strategies.

Optimal sizing and dispatch for a community-scale potable water recycling facility

Optimization dispatch with flexible load participation in new power systems significantly enhances renewable energy accommodation, though the potential of flexible loads remains underexploited. To improve renewable utilization efficiency, promote wind/PV consumption and reduce carbon emissions, this paper establishes a low-carbon economic optimization dispatch model for active distribution networks incorporating flexible loads and tiered carbon trading. First, a hybrid SSA (Sparrow Search Algorithm)–CNN-LSTM model is adopted for accurate renewable generation forecasting. Meanwhile, multi-type flexible loads are categorized into shiftable, transferable and reducible loads based on response characteristics, with tiered carbon trading mechanism introduced to achieve low-carbon operation through price incentives that guide load-side participation while avoiding privacy leakage from direct control. Considering the non-convex nonlinear characteristics of the dispatch model, an improved Beluga Whale Optimization (BWO) algorithm is developed. To address the diminished solution diversity and precision in conventional BWO evolution, Tent chaotic mapping is introduced to resolve initial parameter sensitivity. Finally, modified IEEE-33 bus system simulations demonstrate the method’s validity and feasibility.

With the increasing penetration of wind power, the uncertainty associated with it brings more challenges to the operation of the integrated energy system (IES), especially the power subsystem. However, the typical strategies to deal with wind power uncertainty have poor performance in balancing economy and robustness. Therefore, this paper proposes a distributionally robust joint chance-constrained dispatch (DR-JCCD) model to coordinate the economy and robustness of the IES with uncertain wind power. The optimization dispatch model is formulated as a two-stage problem to minimize both the day-ahead and the real-time operation costs. Moreover, the ambiguity set is generated using Wasserstein distance, and the joint chance constraints are used to ensure that the safety constraints (e.g., ramping limit and transmission limit) can be satisfied jointly under the worst-case probability distribution of wind power. The model is remodeled as a mixed-integer tractable programming issue, which can be solved efficiently by ready-made solvers using linear decision rules and linearization methods. Case studies on an electricity–gas–heat regional integrated system, which includes a modified IEEE 24-bus system, 20 natural gas-nodes, and 6 heat-node system, are investigated for verification. Numerical simulation results demonstrate that the proposed DR-JCCD approach effectively coordinates the economy and robustness of IES and can offer operators a reasonable energy management scheme with an acceptable risk level.

The large-scale integration of renewable energy has reduced power system flexibility and exacerbated supply–demand imbalances. In industrial parks, the combined variability of high energy-consuming industrial loads and photovoltaic (PV) generation further complicates the energy management challenge. Aiming to enhance the operational flexibility of industrial parks and mitigate supply–demand imbalances, this paper proposes a multi-time-scale stochastic energy management strategy that accounts for the uncertainty associated with PV generation. First, a conditional generative adversarial network (CGAN) is employed to generate the representative PV generation scenarios, thereby enabling the modeling of PV generation uncertainty within the optimal dispatch model. Considering the coupling mechanisms and control characteristics of various regulation resources within the industrial park, a multi-time-scale dispatch model is developed. In the day-ahead dispatch phase, the operational costs are minimized by optimizing the production plans of industrial loads. In contrast, in the intraday phase, the more flexible measures, such as adjusting the tap positions of arc furnaces and controlling the charge/discharge of energy storage systems, are employed to smooth power fluctuations within the park. A case study validated the effectiveness of the proposed approach, demonstrating a 7.56% reduction in power fluctuations and a 4.34% decrease in daily operating costs. These results highlight the significance of leveraging industrial loads in park-level systems to enhance cost efficiency and renewable energy integration.

Multi-heat source co-heating networks have become the main trend of future development. However, the research for the thermal optimization and dispatch of multi-heat source co-heating networks is quite few in our country. In this paper, the general process of the optimal dispatch for multi-heat source co-heating systems is proposed, and the mathematical model for optimal thermal dispatch of multi-heat source co-heating systems is established. In addition, formulas and parameter selection method for the typical thermal schemes were given. By taking an actually heating network as an example, procedure and method for some thermal dispatch schemes are provided, and the optimal operating scheme based on technical and economic analysis is given. The method provided in this paper may supply the theoretical basis for operation scheduling of realistic engineering heating network. It is very meaningful for saving energy and improving environment.

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.