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The renewable energy system integrates various power generating units, heating units, energy conversion devices, and energy storage devices, which is an effective way to improve energy efficiency and realize clean energy utilization. In this paper, a renewable energy system integrating with photovoltaic electric power generation systems, wind‐driven generators, gas‐driven generators, gas‐fired boilers, electric chillers, electric and heating energy storage devices is constructed in an isolated tourist area. Considering the complex multi‐energy flows in renewable energy systems, an energy hub with power and heat is established to concentrate and distribute electric and heating energy flow for balancing energy supply and demand in a CCHP system. Introducing the constraints of renewable energy penetration index and energy supply reliability index, a collaborative optimization framework for system configuration is constructed to minimize annual capital cost, fuel cost, and maintenance cost. The simulation results show that the model and algorithm in this paper are feasible and applicable, and the optimization scheme has good economic benefits and optimal performance, which can promote the consumption of renewable energy and reduce the surplus of electric energy and heat energy.

The multi-energy system is a promising energy-efficient technology to supply electric and thermal energy to end-users simultaneously, which can realize the energy cascade utilization. However, it is challenging to optimize the operation of multi-energy systems due to their inherent structural complexity, as well as the highly coupled nature of multiple energy flows and the uncertainty of renewable energy generation. This paper proposed a collaborative demand-controlled operation strategy for a multi-energy system, which consists of an upper-level model and a lower-level model. In the upper-level model, a robust linear optimization method is adopted to optimize the system operation in a day-ahead stage. In the lower-level model, a stochastic rolling optimization method is applied to achieve a dynamic adjustment to cope with the fluctuation in both renewable electricity generation and electric load. The multiple energy demand-controlled strategy is also applied in the optimal operation strategy to achieve load shifting and to create flexibility in energy demand despite the “source-load” imbalance power fluctuation. A case study is carried out and simulation results verify the effectiveness and correctness of the proposed model of the coordinated operation framework.

A multi-energy system can supply both electric and thermal energy simultaneously to the end-users to achieve a high energy use-efficiency. However conventional operational strategies for multi-energy systems, in the existing works, do not consider renewable energy generation plants and CCHP plants as belonging to different interests. To cope with this problem, this article studies a cooperative operation framework model for a Wind-Solar-CCHP Multi-energy system. Instead of the conventional non-cooperative solution-based method, the optimal operation scheduling problem of a multi-energy system is described as an optimization problem of Nash Bargaining. Then the optimization problem of Nash Bargaining for the multi-energy system decomposed into two subproblems: social welfare maximization problem (P1) and energy trading payment problem (P2). The optimal energy transaction profiles can be obtained by solving the P1 problem and the problem of payment of energy trading between PV, WT plant agents and the CCHP plant agent can be obtained by solving the P2 problem. Both problems P1 and P2 can be formulated into mixed-integer linear programming problems and solved by the alternating directional multiplier method. Numerical studies demonstrate the correctness and effectiveness of the proposed cooperative operation framework and distributed algorithm for solving the two subproblems. Moreover, the bargaining-based cooperative operation method can further induce the multi-energy system to improve the operating profit with the reduced feed-in tariff of distributed generation.

A stochastic fuzzy chance-constrained programming model with multi-objective optimization for coordinated control of power loss and voltage in distribution systems with renewable energy is presented by taking power output of DGs and charging-discharging power of EVs as random fuzzy variables and load power as random variables. Considering the fuzziness and randomness of active power output of distributed generation systems with wind and solar energy and charging power of electric vehicles, the key parameters of probability density function are determined by fitting incomplete data of uncertainties such as wind speed, sunlight intensity and charging power of electric vehicles. According to the principle of random fuzzy compatibility, the probability density function of the uncertainties is transformed into the probability distribution function of the uncertainties. The NDC(Normal Distribution Crossover)-based non-dominated sorting genetic algorithm is used to solve the optimization problem, and the Pareto solution set of the multi-objective optimization problem is obtained. The feasibility and applicability of the proposed model and algorithm are verified by simulating IEEE-33 and IEEE-118 distribution system.

Distributed generation and energy storage brings opportunities and risks of distribution systems, such as greater power loss and abnormal voltage fluctuation. A Q-learning powered optimization model for these risk control of is presented in this paper. Considering the uncertainties of output power of distributed generation systems, charging and discharging power of electric vehicles and energy storage devices, a CVaR-based energy risk control model for distribution system with renewable energy is presented to determine the control value of output power of distributed generation systems. Q-learning powered adaptive differential evolution algorithm is used to solve the proposed optimization problem. Second order cone programming is used to simplify the objective function and constraints of the optimization model, and Q-learning driven adaptive differential evolution algorithm is used to enhance the ability of solving, simplify the calculation, and make the solution faster and more stable. The feasibility and applicability of the proposed model and algorithm are verified by simulating IEEE-118 distribution system.

The optimal operation of a combined cooling, heating and power (CCHP) system depends on its structure and adopted energy dispatch strategies. This paper proposes a matrix modeling method for the CCHP system structure, in which multi-energy supply is regarded as the input of system, and cooling, heating, electric load as the output of the system. The energy flow from the system input to output includes the scheduling matrix, the efficiency matrix, and the energy conversion matrix model. Adopt mixed rolling-horizon and particle swarm optimization algorithm to allocation the system scheduling factor to promote optimal operation of CCHP systems. According to the characteristics of input and output energy flow in different seasons of a certain area, the system is simulated and calculated. The results show that the adoption of rolling-horizon optimization for the thermal-electric load in winter can fully calculate the three scheduling strategies and select the optimal strategy in the rolling window. Compared to other methods, the optimization of scheduling factors in summer highlights the low-cost benefits.

The influence and the potential risk due to hidden faults of a relay protection system on power supply in distribution systems are paid more and more attention to. A probability analysis method is used to analyse fault characteristics and action mechanism of dominant faults, hidden misoperation and non-operation of the relay protection systems, and failure probability model of relay protection system is constructed and simplified. The effects of dominant faults, hidden misoperation and non-operation of the relay protection systems on the reduced power supply load power are analysed, and a probabilistic model for reduced power supply load power is constructed by three parts corresponding to dominant faults, hidden misoperation and non-operation. A probability calculation method of power supply risk occurrence due to hidden faults of relay protecttion system is proposed considering the fault probability of the relay protection systems, the frequency of the hidden faults occurring in operation period, the reduced power supply load power or load power outage, and the connection mode of the in-lines, out-lines and transformers in a substation. The feasibility and applicability of the proposed method for estimation of risk value probability of the relay protection systems is verified by two studied examples.

A modeling framework of a multi-energy system with coordinated supply of combined cooling, heating and power (CCHP) using wind and solar energy is established in this paper. An index of loss of power supply probability and loss of heat supply probability is constructed defining a reliability index for the supply of the specific form of energy. Considering and weighing investment cost, primary energy consumption, carbon dioxide emission and the newly defined energy supply reliability index, an optimization model for capacity allocation of multiple energy sources in a multi-energy system using wind, solar, gas and storage is constructed using Particle Swarm Optimization (PSO) algorithm to solve the optimization problem. The proposed method increases renewable energy generation rate, reduces energy storage capacity and improves energy supply reliability.

A risk probability classification model of distribution system with source-network-load-vehicle is established based on fuzzy C-means clustering and fuzzy mathematics in this paper. Monte Carlo simulation method is used to simulate various scenarios of operation and failure of lines, transformers, generators in distribution systems. The data needed for probability classification of voltage fluctuation, line overload, power loss and load loss of distribution system are obtained. For the severity of the accident, the membership functions of voltage fluctuation, line overload, power loss and load loss are constructed by using fuzzy distribution functions based on fuzzy classification method, and the corresponding risk severity classification model is constructed. Mamdani fuzzy reasoning is used to synthesize the risk probability and risk severity for determining the risk level. A risk early warning method for distribution system based on fuzzy C-means clustering is proposed. Taking IEEE-33, IEEE-39 and IEEE-118 systems as studying examples, the risk levels of voltage fluctuation, line overload, power loss and load loss are determined with the cases of normal operation and anticipated fault in distribution system.

A dynamic generation flow strategy using for dynamically coordinating water head of reservoir and water consumption volume for electric energy production is presented in this paper. A novel multi-objective scheduling model is proposed to achieve the optimal trade-off between water volume for generation and electric quantity. To realize the optimal power output the coordination condition is used to describe the relationship between the current water head level and generation flow rate. The constraints are presented in an efficient method based on characters of the cascaded hydroelectric plants. A hybrid global optimization method, which can overcome the shortcoming of weighted method, is proposed for solving the multi-objective problem efficiently. This is done by embedding the data envelopment analysis (DEA) into an electromagnetism-like algorithm (EMA). A test system with eight hydroelectric plants was used to verify this new method. Results show that this novel scheduling method can enhance the synthesis generation benefit of the cascaded hydroelectric plants and realize the optimal solution for the scheduling model.

A numerical differentiation based algorithm for parameter measurement of low-voltage distribution systems in transient processes is presented in this paper. In a transient process of RL circuit, there is non-periodic component with exponential decay and periodic component of sinusoidal waveform. In any transient processes of RL circuits, the proposed algorithm may not only accurately track the frequency of distribution systems, and estimate the amplitude and phase angle of sinusoidal component, but also estimate the amplitude and time constant of the non-periodic component. The simulation results of a studying example carried out for non-noise cases in Matlab show high accuracy, simple computation and less time, the simulation results carried out for noisy cases and harmonic cases may see acceptable errors, simple computation and less time.

A novel strategy for optimal scheduling of short-term electric power generation of cascaded hydroelectric plants based on particle swarm optimization (PSO) and chance-constrained programming is presented to maximize the expected profit at a given risk level in this paper. Based on chance-constrained programming, in which some specified probability are given to simulate some uncertainties, such as water inflows, electricity prices, unit status, and so on. This paper proposes a model for short-term scheduling optimization of cascaded hydro plants, which includes uncertainties, spatial-temporal constraints among cascaded reservoirs, etc. A hybrid particle swarm optimization (HPSO), which is embedded with evolutionary algorithms, is presented to use for the solution of global optimization problems. Catastrophe theory, which is concerned with natural evolutionary or survival-of-the-fittest, is utilized as an indication of the premature converge of PSO, and the positions of particles are further adjusted in the search space according to chaos optimization. In this way, each particle competes and cooperates with its neighbors. The proof shows that HPSO is guaranteed to converge to the global optimization solution with probability one. The model presented is solved by a combination method of HPSO and Monte Carlo simulation. Finally, a numerical example is served for demonstrating the feasibility of the method developed.

A numerical differentiation-based algorithm for high-accuracy, wide-range frequency estimation of power systems is proposed in this paper. The signal includes up to 31st-order harmonic components. Using the central numerical differentiation of multipoints on the basis of Lagrange interpolation, the estimated frequency of nonsinusoidal signals of power systems can be measured at an accuracy of 99.999% over a wide range of from 10 to 100 Hz with fundamental amplitude varying from 100 to 300 V and with a fundamental phase angle varying from 0 to 360. The proposed algorithm needs at most two cycles or 40 ms for estimation of frequency over 40 to 60 Hz, and needs at most three cycles or 60 ms over 10 to 40 Hz or 60 to 100 Hz. Using Matlab software, the simulation results of the test example are satisfactory.

Considering distinct locations and inhomogeneous power plants playing different roles and effecting on the interconnected power system, as well as the purpose of pursuing best efficiency and stability for the whole interconnected power system, this paper presents a new method based on sensitivity analysis and neural network to solve the power regulation on tie-line in the interconnected power system. The sensitivity coefficients for the power plants relating to tie-line can be calculated and simulated by back propagation neural network, before which the appropriate samples setting of power plants should be acquired adequately from operating BPA software. The effectiveness of the proposed method is demonstrated on IEEE 30-bus system comprising of plants units of actual power grid in different regions and compared with some conventional approaches. The simulation results show that the proposed new approach is able to obtain higher quality solutions efficiently than the conventional approaches.

A Neuro-fuzzy and digital FIR filtering approach for denoising in power system measurement and control is presented in this paper. The signal is denoised via two processes. First, the signal is denoised based on fuzzy neural network filtering algorithms, and then the signal is once again denoised using digital FIR filtering methods. The simulation tests carried out in Matlab show much better results of denoising using Fuzzy-Neuro and digital FIR filtering approach than using one of them only. The proposed method also shows vely good advantages for both static signals and transient signals.

A fuzzy random chance-constrained programming method for quantifying transmission reliability margin is presented in this paper. Uncertainty problem of power systems is modeled as a fuzzy stochastic optimization problem, solved the hybrid intelligence algorithm based on chance constrained programming model. The simulation results of IEEE30 system demonstrate the advantages of the proposed approach.

This paper present three methods for tracking the fundamental frequency of power systems in digital protection relaying, which is based on numerical differentiation, hardware and digital filtering respectively. The numerical differentiation method is adaptive only to pure sinusoidal cases, and hardware and digital filtering method is used not only for pure sinusoidal cases but also non-sinusoidal cases. The numerical differentiation method is characteristic of high accuracy, lesser time and computation. The hardware and digital filtering method employs hardware and digital filter to decompose the fundamental component out of the non-sinusoidal signals, and then uses numerical differentiation algorithm to track the fundamental frequency.

A bilateral trade model for capacity payments is presented in this paper. The procedure is based on an organized market where IPP (independent power producer) and LSEs (load serving entities) or large customers can trade the capacity just like energy. So both the price and allocation among the different plants and LSEs are determined through competitive mechanisms. This results in the stabilization of the income of the generators and provides a clear incentive for new generation investment. It also shows how much capacity the customers need in the future and how much they are willing to pay. Additionally, the relationship between energy market and the capacity payments also can be used to mitigate the highly volatile spot price. The proposed model has been tested with a study case and some computation results have been demonstrated. Nash equilibrium game is used to compare the abuse of market power in two type of capacity market.

A technique for tracking the frequency of power systems in sine noises using numerical differentiation is presented in this paper. A voltage or current sinusoidal signal corrupted by a sine noise with the frequency varying from 0 to 300 Hz, the amplitude varying from 0 to 300 V, and the phase angle varying from 0 to 360 degree is taken into consideration. Using central numerical differentiation with 7 points, the proposed algorithm shows more advantage in time and speed compared with other existing techniques, and shows better dynamics and high accuracy infrequency estimation. The simulation results of a test example carried out in Matlab are satisfactory.

Summary form only given. A numerical differentiation based algorithm(NDBA) for high-accuracy, wide-range frequency estimation of power systems is proposed in this paper. The signal includes up to 31-order harmonic components. Using the central numerical differentiation of multi-points on the basis of Lagrange interpolation, the estimated frequency of non-sinusoidal signals of power systems can be measured at an accuracy of 99.999% over a wide range of from 10 Hz to 100 Hz with fundamental amplitude varying from 100 V to 300 V and with fundamental phase angle varying from 0 to 360. The proposed algorithm needs at most 2 cycles or 40 ms for estimation of frequency over 40 Hz to 60 Hz, and needs at most 3 cycles or 60 ms over 10 Hz to 40 Hz or 60 Hz to 100 Hz. Using Matlab software, the simulation results of the test example are satisfactory.

An algorithm for instantaneous estimation of sinusoidal signals basing on numerical differentiation is presented in this paper. With all parameters unknown, the signal is sampled at a fixed frequency, and central Lagrange interpolation of multi-points is employed for computation. The proposed algorithm is adaptive to any sinusoidal signal with its frequency varying from 1 Hz to 4 kHz, its amplitude varying from 0.1 V to 100 V, and its phase varying from 0 to 360. The proposed algorithm for frequency estimation needs in most 1 cycle, with an accuracy of more than 0.001%. An example for test study is simulated in Matlab software with satisfactory results.

Stochastic models for optimal generation production in electricity markets, including a model for optimum quantities of the whole electric power plant and a model for optimum unit loading scheme of generating units, a new genetic algorithm for the solution of these optimization problems are presented in this paper. The generation production optimization problem which is a stochastic optimization process is solved by decomposing the original problem into piecewise linear function. In the proposed problem, the generating units with different unit production costs, unit holding costs and unit shortage costs are loaded to produce electrical quantities in order to meet time-varying stochastic demand in a planning period. The optimization problem is to determine the electrical quantities to minimize the expectation of production costs, with a consideration of holding costs, shorting costs, start up costs and shut down costs. A study case is given to illustrate the results of the proposed model.

Two models for trust transaction are presented in this paper. Trust transaction comes into being when there are transmission congestion and unit faults. Trust transactions provide generators with higher marginal costs more chances to put into the network for generation, and trust transactions are one of main measure for solving the problem of transmission congestion and unit faults. Generators can bid in four markets proposed in this paper, which are power market, reserve market, sharp-load market and trust market respectively. The market prices of trust market are always the same as the previous contracts, so that some generators with greater marginal costs can acquire much more revenue in the trust market to maximize their generation profits. The proposed models have been tested with a study case and some computation results have been demonstrated.