Combined Cooling, Heating And Power Microgrid Research Articles

Risk‐based scheduling of CCHP‐based microgrid considering economic and environmental aspects using confidence degree theory and CVaR approach

AbstractThe combined cooling, heating, and power (CCHP) co‐generation system is an alternative for developing sustainable energy systems. Inside a multi‐energy CCHP microgrid, electric, heat, and cool demands are supplied with a high efficiency. Integrating various energy conversion technologies and storage systems allows managing different resources and taking advantage of electric market participation. However, the uncertainties associated with source demand and prices should be taken into account. In this regard, this paper proposes stochastic programming to optimize the operation cost and emission penalty of a multi‐energy CCHP microgrid considering the mathematical model of components and related uncertainties. Using the proposed optimization problem, the system operator can derive bidding/offering curves in the electric market. To mitigate the financial risks, the conditional value‐at‐risk (CVaR) approach is integrated to provide different risk‐averse strategies. From the results, it is found that under the risk‐averse strategy, by paying 0.4% more money, the risk of CCHP's operation cost instability will be reduced by approximately 16.82%.

Joint energy management and trading among renewable integrated microgrids for combined cooling, heating, and power systems

Energy depletion and rising demand cause a demand-supply imbalance and power system instability. Centralized energy production makes the system less reliable and less efficient because of problems with transmission and line losses. Conventional microgrids use electricity to heat, cool, and power loads, wasting microturbine surplus heat. To address these issues, a joint energy management and trading model for combined cooling, heating, and power (CCHP) microgrids is proposed to minimize the system cost as well as enhance the system’s efficiency and reliability. The framework is designed in such a way that, in the first step, each CCHP microgrid will apply demand-side management to meet its load demands from its own generation. If any CCHP microgrid does not have enough self-generated energy to meet its load demands, then it can purchase energy from nearby CCHP microgrids or utility. On the other hand, each CCHP microgrid has the option to sell any extra energy to nearby CCHP microgrids or utility. Furthermore, each CCHP microgrid can use a diesel generator to meet its energy demands if the available energy from its own generation and energy purchased from other CCHP microgrids or utilities is not enough. To solve the problem of integer linear programming, the branch, and bound algorithm is used. The results show a reduction in the system’s overall cost by 33.29% whereas peak energy demand is reduced by 25.76%. Reduction in peak energy demand will minimize the line losses, and hence the system efficiency and reliability will be improved.

Low-carbon Economic Scheduling of Combined Cooling, Heating and Power Microgrid Considering Demand Response and Shared Energy Storage

In order to further promote the new energy consumption of combined cooling, heating, and power (CCHP) microgrid, reduce carbon emissions, and optimize the total operating cost of the microgrid, a low-carbon optimal scheduling model of CCHP microgrid was proposed. It considered demand response (DR) and shared energy storage (SES). Firstly, the concept of SES and its operation mode are applied to the microgrid. Secondly, in order to take advantage of the flexible nature of multiple loads, an integrated demand response (IDR) model with a ladder subsidy mechanism was established. Then, the optimal scheduling model introduces a reward and punishment ladder carbon trading mechanism (RPL-CTM). Finally, taking energy purchase cost, load reduction subsidy cost, interaction cost with energy storage station, carbon trading cost, and wind abandoning penalty cost as optimization objectives, a multi-energy coupling, and multi-objective collaborative optimization scheduling model was established. Numerical analysis indicates that the model can efficiently lower the microgrid’s operating cost and encourage the use of clean energy and carbon emission reduction.

The capacity optimization of the battery energy storage system in the combined cooling, heating and power microgrid

In the microgrid, the battery energy storage system (BESS) is used in combination with renewable energy to solve the problem of renewable energy accommodation. Meanwhile, power sources with the BESS can participate in valley filling and reduce electric generation costs. However, the BESS cannot contribute sufficient capability to the microgrid and reduce costs due to its limited capabilities. To tackle this challenge, the output power model of the combined cooling, heating and power microgrid (CCHPM) with the BESS is proposed in this paper. The model takes the micro-gas turbine, the photovoltaic unit, the wind turbine, the electric refrigerator and the BESS as a whole CCHPM. Meanwhile, a capacity optimization method of the BESS is developed to select the appropriate capacity and promote renewable energy accommodation. The minimum cost of the objective function is established. The electric power balance of the CCHPM, charge state constraints of the BESS, capacity constraints and ramp-up/down constraints of the units are derived to optimize the output power of units and the capacity of the BESS. The effectiveness of the proposed method is verified by the system simulation of YALMIP and GUROBI. Compared with the traditional method, the total cost of the microgrid is decreased by 1.4%, and the microgrid’s renewable energy accommodation is enhanced by 12.2%. This capacity optimization method can enhance the applicability of BESS in CCHPM.

Multi-objective optimal dispatching of combined cooling, heating and power using hybrid gravitational search algorithm and random forest regression: Towards the microgrid orientation

In order to eventually reach the net-zero carbon target, the low carbon transition demands substantial growth in the penetration of renewable generation in energy systems. It is essential to have enough flexible resources to prevent the curtailment of energy systems with high intermittent renewable output to run consistently. Therefore, this study focuses on the multi-objective optimal dispatching model of combined cooling, heating, and power (CCHP) microgrid with power-to-gas (P2G). The structure of the CCHP microgrid and the working principle of P2G are studied and analyzed. First, briefly described the multi-objective optimal dispatch model of the CCHP microgrid with P2G established with the goal of operational and environmental cost. Second, a hybrid gravitational search algorithm and random forest regression (GSA-RFR) are introduced to find out the optimal values. The proposed model verifies that P2G improves the wind abandonment capacity of the CCHP microgrid operation system. Furthermore, it reduces environmental costs and transforms environmental values into economic benefits during system operation. Noting that, the GSA-RFR improves the system economy by 7.13% comparatively.

Study on Economic Dispatch of the Combined Cooling Heating and Power Microgrid Based on Improved Sparrow Search Algorithm

The reasonable and efficient use of the abundant biomass resources in rural areas has not been realized. Therefore, the concept of a combined cooling, heating, and power (CCHP) microgrid system, considering biomass pyrolysis and gasification, has been developed by researchers. A biomass gasification device can fully use biomass resources and can play a role in absorbing wind energy. Meanwhile, in order to minimize the operating cost of each micropower supply unit, as well as the environmental pollution costs, researchers have also established an optimal scheduling model for CCHP microgrids, which uses the sparrow search algorithm. In this paper, we have improved upon the traditional sparrow algorithm to solve the problems of its uneven population distribution, poor global search ability, and the risk of falling into local optima, through the development of the random walk sparrow search algorithm (RSSA). First, a sinusoidal chaotic map is used to generate the early-generation sparrow population with a uniform distribution in space. Second, in this study we add a sharing factor to the discoverer’s optimization process to enhance information sharing and the global research capability among individuals in this field. Finally, a random walk strategy is used to form new participants to improve the algorithm’s skill in locally searching for optimal locations. Taking the CCHP microgrid with grid-connected action as a case study, we concluded that compared with the optimization outcomes of the SSA, the total costs incurred by RSSA in summer and winter were reduced by 2.2% and 3.1%, respectively. Compared with the optimization findings for the chaotic SSA algorithm, the total costs incurred using the RSSA algorithm under typical summer and winter days were reduced by 0.14% and 0.13%, respectively. The productiveness of the RSSA algorithm for solving the CCHP microgrid economic dispatch issues has thus been verified.

Tri-Level Integrated Optimization Design Method of a CCHP Microgrid with Composite Energy Storage

Combined cooling, heating, and power (CCHP) microgrids are important means of solving the energy crisis and environmental problems. Multidimensional composite energy storage systems (CESSs) are vital to promoting the absorption of distributed renewable energy using CCHP microgrids and improving the level of energy cascade utilization. In this context, this paper proposes a multi-energy coupling structure that includes a multidimensional CESS with a compressed air energy storage (CAES) connected to a CCHP microgrid. Dividing design and operation causes some problems, such as low operating efficiency and difficult energy matching of CESSs. To solve the existing problems, an integrated design method is proposed that considers the capacity configuration of the equipment and the optimal operation of the system on a multi-timescale. The optimization result of the capacity configuration level is used as the constraint of the operational control level, and the equipment output plan of the operational control level is used as the optimized operation strategy and parameters of the system. The C-NSGA-II algorithm is adopted at the capacity configuration level and day-ahead scheduling level. Rolling optimization is solved using the PSO algorithm. The final result that satisfied the output design was obtained after several iterations. The average daily cost and CO2 emission reduction rate (CO2ERR) of capacity configuration levels are $2241 and 45.02%. The best CO2ERRs of day-ahead scheduling optimization levels are 39.9% and 45.9% in summer and winter, where the operating cost saving rate (OCSR) are 30.5% and 38.3% separately. Examples show that the integrated design method presented in this paper has significant advantages in enhancing energy-grade matching and improving the economy and environmental protection of the system.

An optimal dispatch model of adiabatic compressed air energy storage system considering its temperature dynamic behavior for combined cooling, heating and power microgrid dispatch

Adiabatic compressed air energy storage (A-CAES) technology naturally has the ability of cogenerating cooling heating and electric power. It is a promising energy storage technology in the application of combined cooling, heating and power (CCHP) dispatch. This paper explores a new modelling method of the A-CAES for CCHP dispatch by taking the temperature dynamic behavior of the A-CAES into detailed consideration. With the help of the binary technique and piecewise approximation method, this A-CAES model is formulated based on mixed-integer linear programming (MILP), which can be easily solved. On the basis of this, an optimal day-ahead dispatch model for a CCHP microgrid containing an A-CAES is proposed. The empirical study based on a pilot plant and system data in China is carried out. The simulation results verify the effectiveness of the proposed A-CAES model. With the comparison of the results using the proposed A-CAES model and the conventional A-CAES model without considering temperature dynamic behavior, the results show that the system operation cost obtained by the proposed model can be 13.7% lower than the results obtained by the conventional A-CAES model.

Multi-time scale dynamic robust optimal scheduling of CCHP microgrid based on rolling optimization

Combined cooling, heating and power (CCHP) microgrid can effectively improve the efficiency of renewable energy and reduce the operating cost of microgrids. However, the uncertainties of renewable energy sources and variety load demands make the optimal scheduling of CCHP microgrid complex and difficult to achieve. In this paper, a novel multi-time scale dynamic robust optimal scheduling strategy is proposed for the coordinated operation of CCHP microgrid, which includes two time scales: the day-ahead scheduling scale and the intraday adjustment scale. In the day-ahead scheduling scale, the uncertainties of CCHP microgrid are introduced by describing the day-ahead forecast data as interval numbers with upper and lower bounds. The scheduling decision-making variables and the interaction power with power grid are obtained by considering the regulation cost in the worst-case scenario. In the intraday scheduling operation scale, the rolling optimization strategy is applied to obtain intraday optimal power allocation by minimizing the scheduling cost at each prediction horizon based on the intraday ultra-short-term forecast data, which preserves the day-ahead scheduling decision. Finally, the power deviation of the intraday ultra-short-term optimal power allocation is smoothed based on real-time operating data. The simulation results demonstrate that the proposed strategy can effectively reduce the operating cost and improve the robustness for the coordinated operation of CCHP microgrid.

Economy-environment-energy performance evaluation of CCHP microgrid system: A hybrid multi-criteria decision-making method

The combined cooling, heating and power micro-grid (CCHP-MG) system has significant economy-environment-energy (3E) characteristics, making it valuable to evaluating its 3E performance in promoting the 3E coordinated development. Based on the energy supply structure of CCHP-MG system, this paper constructs a 3E performance evaluation index system for the CCHP-MG system from economy, environment and energy dimensions. Then, a hybrid multi-criteria decision-making (MCDM) framework is designed, involving integrated weighting method based on anti-entropy weight method and grey decision-making trial and evaluation laboratory (grey-DEMATEL), and improved technique for order preference by similarity to an ideal solution (TOPSIS) with difference and quotient grey relation analysis (DQGRA). 8 building-typed CCHP-MG systems are taken as examples for analysis. The results show that the benefits of system energy supply, equivalent emission reduction of pollutants, energy utilization and equivalent energy saving are important manifestations of CCHP-MG's 3E performance, and raising energy supply scale and renewable energy proportion are the key ways to improve CCHP-MG's 3E performance. Comparing with 4 other MCDM models, the proposed hybrid MCDM model has good applicability and effectiveness for the 3E performance evaluation of CCHP-MG system, but it is necessary to carry out targeted adjustment when applying it to other MCDM issues.

Multi-objective economic optimization scheduling of CCHP micro-grid based on improved bee colony algorithm considering the selection of hybrid energy storage system

By factoring into the model selection of a hybrid energy storage system, this paper established an economically optimized multi-objective dispatching model for a micro-grid of Combined Cooling Heating and Power (CCHP) based on an improved Algorithm of Artificial Bee Colony (ABC). To upgrade the single-objective model in previous studies, the optimization goal of the model is to minimize the daily power generation dispatching cost and daily environmental pollutant treatment cost of the micro-grid, and the improved Algorithm of ABC based on Beetle Antennae Search Algorithm (BAS-ABC) was used to obtain a solution. To achieve a better application of theoretical results, the paper studied the summer typical daily real power load data of a grid-connected CCHP micro-grid in a district of Shanghai, and simulated a multi-objective economical dispatching model under the condition of using the Time of Use (TOU) billing system. The result shows that the minimum cost derived from the solution of the optimized ABC is lower than that from the traditional ABC. Furthermore, when the objective function of the CCHP microgrid is the lowest power generation cost or the lowest environmental cost, the optimized results are contradictory: it is impossible to simultaneously achieve an optimal power generation cost and an optimal environmental cost. This indicates the multi-objective optimized dispatching approach has comprehensively balanced the economic efficiency and the environmental-friendliness of the system.

An improved two-stage robust optimization model for CCHP-P2G microgrid system considering multi-energy operation under wind power outputs uncertainties

The combined cooling, heating and power (CCHP) microgrid has the advantages of promoting cleaner production and improving energy utilization efficiency. With the development of renewable energy sources (RES), it is more and more significant to study the optimal operation of CCHP based on the uncertainties of RES outputs. This paper proposes a CCHP-P2G microgrid system, which combined the power-to-gas (P2G) device with traditional CCHP microgrid. And a Data-driven Set based robust optimization (DSRO) model considering the uncertainties of wind power and multiple demand response programs (DRPs) have been presented, which consists of two stages: Day-ahead dispatching stage and Real-time adjusting stage. Simulations are delivered to show the following outcomes: (1) the P2G device can improve the electricity-gas coupling in the CCHP-P2G system, enhancing the system’s stability and economy of the system operation; (2) the multiple DRPs in the DSRO model can play the role of peak shaving and valley filling of electrical load, heating load and cooling load, so as to further reduce the operating cost of the system; (3) the DSRO model can resist the interferences of uncertain wind power outputs and keep both the conservativeness and computational complexity in relatively low levels.

Research on Comprehensive Value of Electrical Energy Storage in CCHP Microgrid with Renewable Energy Based on Robust Optimization

The combined cooling, heating, and power (CCHP) microgrid system is good for energy gradient utility. At the same time, it can promote the renewable energy (RE) consumption and abate environmental pollution. In a CCHP microgrid system, the electrical energy storage (EES), which can storage and release electrical energy, plays an indispensable role. A robust optimization model of the CCHP microgrid participating in power market transaction is constructed to calculate the CCHP microgrid operation cost in 4 cases. The results show that the EES can significantly reduce the cost of the CCHP microgrid by 13.21%, compared with 8.36% in Group 1 without renewable energy. The EES can reduce the reserved capacity of micro gas turbine units to deal with the precariousness of RE generation and then reduce the CCHP microgrid operation cost by reducing the purchase of energy from the power grid and arbitrage. Finally, the calculation method of comprehensive value of the EES is constructed. The comprehensive value of the EES is higher in Group 2 with renewable energy compared with Group 1 without renewable energy. Through net present value (NPV) calculation and sensitivity analysis, it is found that the RE penetration level and EES cost have the greatest impact on the economic performance of EES. This shows that with the continuous rising of the RE penetration level and the gradual decrease of EES cost, great potential still waits to be tapped in the comprehensive value of EES in the future.

Hierarchical two‐stage robust optimisation dispatch based on co‐evolutionary theory for multiple CCHP microgrids

Combined cooling, heating, and power (CCHP) microgrids are a special form of a microgrid that is attracting increasing attention. This study contributes to the goal of minimising the operation cost of CCHP microgrids by proposing a hierarchical two-stage robust optimisation dispatch model for multiple CCHP microgrid systems. The uncertainties associated with wind power output, electric power, heating, and cooling loads, and transmission line failures are considered in the proposed model. Moreover, the electricity purchasing and selling prices of each microgrid are independently determined. The proposed model applies the outputs of fuel cells, energy storage devices, and gas turbines, the distribution factor of waste heat, and the power transmission between the microgrids and an external grid as control variables. The optimised dispatch problem is solved using McCormick envelopes relaxation and a novel column and constraint generation algorithm that provides enhanced optimisation performance by implementing co-evolutionary theory. In this way, the microgrid system is divided into several sections, and each section is represented as an individual min–max–min problem. The rationality and validity of the proposed model and the superiority of the solution performance of the improved algorithm are verified through simulation case studies involving a system composed of four CCHP microgrids.

Integrated operation optimization for CCHP micro-grid connected with power-to-gas facility considering risk management and cost allocation

According to the multi-energy coupling characteristics of CCHP (combined cooling, heating and power) micro-grid and P2G (power-to-gas) facility, their integration provides a new perspective to enhance energy efficiency and clean production. In this paper, an integration framework of CCHP-P2G system is proposed by attaching a P2G facility to a CCHP micro-grid. Consequently, the operation optimization models for CCHP, P2G and CCHP-P2G are constructed based on an improved risk explicit interval parameter programming (REIPP) approach. Firstly, the uncertainties of wind power outputs, electricity and natural gas prices are modeled as interval numbers. Secondly, the risk objective functions are formulated with the interval numbers etc. Thirdly, the REIPP based operation optimization models for CCHP, P2G and CCHP-P2G are presented by employing the risk objective functions, which pursue risk minimization while considering operational cost control. Finally, the REIPP based models are further improved by applying fuzzy theory to reflect the impact of different risk attitudes on the operation strategies. Moreover, considering that CCHP and P2G may have different investors, a Shapley-value cost allocation mechanism is proposed based on the solutions from the improved REIPP (IREIPP) based models. Simulations have verified the effectiveness of our proposed integration framework, IREIPP approach and Shapley-value cost allocation mechanism.

Power Capacity Optimization in a Photovoltaics-Based Microgrid Using the Improved Artificial Bee Colony Algorithm

Although the combined cooling, heating and power (CCHP) microgrid is feasible for achieving a high energy utilization efficiency, the fluctuation of energy sources, such as a photovoltaic system and multiple loads, may affect the safety, economics and stability in CCHP microgrid operation. For this reason, this paper establishes a mathematical model using a multi-objective optimization mechanism for resolving the influence of economy and energy allocation in the mixed photovoltaic type CCHP microgrid. It is based on analytic hierarchy process (AHP) to determine the individual weight of objective function optimization for the multi-objective power capacity allocation. The improved artificial bee colony (IABC) based on the whale search and dynamic selection probability can achieve an optimization solution, reaching a stable operation state and reasonable capacity configuration in the microgrid system. The performance results confirm that the proposed algorithm is superior to others in both convergence speed and accuracyfor the capacity allocation of the CCHP microgrid.

A stochastic-robust coordinated optimization model for CCHP micro-grid considering multi-energy operation and power trading with electricity markets under uncertainties

The combined cooling, heating and power (CCHP) micro-grids are getting increasing attentions due to the realizations of higher energy efficiency etc. The deepening of electricity market (EM) reforms provides an opportunity for CCHP micro-grid to interact with upstream power grid through participating in EMs while being operated. Hence, the multi-energy operation optimization of CCHP micro-grid should be extended to further involve the strategy of power trading with EMs. This paper proposes a stochastic-robust coordinated optimization model for CCHP micro-grid considering multi-energy operation and power trading with EMs, from the perspective of the day-ahead stage. By depicting the uncertain day-ahead and real-time clearing prices as stochastic scenarios, the objective function is constructed based on stochastic optimization and conditional value-at-risk, which is to minimize both the expected operational cost and potential risk of cost increase related to scenarios. By modeling the uncertain renewable generations as uncertainty set, the operational constraints are established based on robust optimization, which is to guarantee safe and stable operation under the worst-case realizations within uncertainty set. Through the abovementioned combination of multiple methods, the proposed model reasonably keeps both the conservativeness and computational complexity at relatively low levels. Simulations have verified the effectiveness of this model in terms of cost minimization, computational time, renewable power accommodation and risk control compared with existing methods. Furthermore, simulation results also reveal that additional benefits are brought for CCHP micro-grid by EM participations.

Optimal planning of multiple energy flows for microgrid with renewable energy sources

With the gradual depletion of fossil energy and the increasingly serious environmental pollution problems, the combined cooling heating and power (CCHP) microgrid with renewable energy sources which with high energy efficiency, less pollution emissions and flexible operation control have become research hotspots. In this paper, the optimal planning of CCHP microgrid with renewable energy sources is studied, and the mathematical model of optimal configuration of the system is established with the minimum total cost as the objective function, then the separated production (SP) system is used as a reference to compare the optimal configuration results of the CCHP microgrid system under the two modes of following the thermal load (FTL) and following the electric load (FEL). After that, the optimization configuration results of the two schemes of whether the CCHP microgrid system is equipped with the thermal storage tank (TST) in the same operating mode are compared. The simulation result shows that the system with TST under the FEL mode is more economical, energy-saving and environmentally friendly. Finally, the effects of natural gas price and electricity price on the optimal configuration results of the system in two operating modes are studied.

A Three-Stage Coordinated Optimization Scheduling Strategy for a CCHP Microgrid Energy Management System

With renewable generation resources and multiple load demands increasing, the combined cooling, heating, and power (CCHP) microgrid energy management system has attracted much attention due to its high efficiency and low emissions. In order to realize the integration of substation resources and solve the problems of inaccurate, random, volatile and intermittent load forecasting, we propose a three-stage coordinated optimization scheduling strategy for a CCHP microgrid. The strategy contains three stages: a day-ahead economic scheduling stage, an intraday rolling optimization stage, and a real-time adjustment stage. Forecasting data with different accuracy at different time scales were used to carry out multilevel coordination and gradually improve the scheduling plan. A case study was used to verify that the proposed scheduling strategy can mitigate and eliminate the load forecasting error of renewable energy (for power balance and scheduling economy).

A Wasserstein based two-stage distributionally robust optimization model for optimal operation of CCHP micro-grid under uncertainties

Combined cooling, heating and power (CCHP) micro-grids are getting increasing attentions due to the realization of cleaner production and high energy efficiency. However, with the features of complex tri-generation structure and renewable power uncertainties, it is challenging to effectively optimize the operation of CCHP micro-grid. This paper proposed a novel Wasserstein based two-stage distributionally robust optimization (WTSDRO) model for the day-ahead optimal operation of CCHP micro-grid. The uncertainties of wind power (or other renewable energy sources with random power output) forecasting errors are modeled as an ambiguity set based on Wasserstein metric, which is assumed to contain all the possible probability distributions with a confidence level. In the first stage, CCHP micro-gird’s operation cost is minimized according to the forecast information. In the second stage, for hedging against the perturbation of random wind power outputs, flexible resources are adjusted under the worst-case distribution within the ambiguity set. Multiple demand response programs (DRPs) are integrated to make electrical, thermal and cooling loads controllable. Finally, a reformulation approach is proposed based on strong duality theory, which equivalently transforms the WTSDRO model into a tractable MILP framework. Simulations implemented on a typical-structure CCHP micro-grid are delivered to show that our proposed model: (1) is data-driven and keeps both of the conservativeness and computational time at relatively low levels, (2) reaches effective operation results in terms of cost optimization, wind power accommodation and waste heat utilization etc. Moreover, operation cost and CO2 emission can be further saved by integrating multiple DRPs.