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Probabilistic multi-energy flow (PMEF) analysis is the fundamental of planning and operation of integrated energy system (IES) considering uncertainties. The injection of hydrogen will make the PMEF calculation more complex and can deeply affect the distribution of energy flows. This paper studies an efficient PMEF calculation method which can obtain the probabilistic information of electricity-gas IES with hydrogen injection. Firstly, the steady-state model of electricity-gas IES with hydrogen injection is established, in which the gas network with hydrogen injection is formulated in compact matrix form. Secondly, the Jacobian matrix for the steady-state model of electricity-gas IES with hydrogen injection is derived. Thirdly, the cumulant method and Nataf transformation are applied to calculate the PMEF considering the correlations. Finally, case studies are carried out on a coupled IES consists of modified IEEE 33-bus electricity system and 11-node gas system. The results demonstrate that the proposed PMEF method can reduce computational time greatly while keep high computational accuracy when compared to the Monte Carlo simulation method.

The replacement of hydrogen with conventional fossil fuel has been viewed as a feasible route to the ZERO emission transportation. Nowadays, the water electrolysis method has become mature and efficient enough to produce large-scale of hydrogen, and the condensation and reliquefaction technologies also greatly reduce the hydrogen loss via pipeline transmission, which both motivate the applications of hydrogen as a fuel. In this study, a combined power-hydrogen network is planned to supply the power demand of residential blocks and the charging/fueling demand for electrical vehicles and hydrogen vehicles, respectively. However, the joint scheduling of power-hydrogen network is challenging due to the non-linearity and non-convexity inherited from the hydraulic dynamics of hydrogen transmission. To resolve this problem, a convex-concave based sequential convex approximation method is proposed. A revised 37-bus system is used to demonstrate the validity of proposed method.

Optimal transmission switching (OTS) is widely used in mitigating transmission congestion and reducing power losses. However, with large-scale renewable energy integration to the grid, making a decision for the randomness characteristic of renewable energy (e.g. wind and solar power) is complicated in OTS. In this study, a novel stochastic optimal power flow-based point estimation method (PEM) is presented to model the uncertainties of wind power and load in OTS. Polynomial normal transformation is introduced to handle the correlations between random variables, and percentile matching method is employed to obtain the coefficients of polynomial normal transformation, which can avoid the integral operation. Moreover, the expected value of power flow obtained by the PEM is applied to the presented model to control the line overload risk. Two indexes, namely, population sample mean and mean of population sample standard deviation, are proposed to investigate the effect of correlations on OTS strategies. The proposed model is finally transformed into a mixed-integer second-order cone programming to consider bus voltage and reactive power, and the modified IEEE RTS 24-bus system and IEEE 118 system are presented to test the model.

High penetration of renewable energy is one of the important feature of future power systems. However, the randomness and intermittence of renewable energy have brought new challenges to planners. To accommodate the uncertainty and variability of wind power, an innovative scenario-based stochastic model that incorporates generation, transmission, and reactive power planning is presented, considering spinning reserve, unit ramp rate, and current output of units. To consider reactive power flow and voltage, a second-order cone programming model based on relaxed AC optimal power flow is introduced. Moreover, unit status and optimal transmission switching are implemented to promote wind consumption, reduce congestion, and attain a more flexible topology. The proposed methodology organically combines operational constraints with planning issues. Finally, two modified test systems with high wind power integration are utilized to illustrate the effectiveness of the presented model.

Utilizing multiple energy supply solutions and the thermal inertia of buildings are considered as an efficient method for improving energy conservation and the operational flexibility of Combined Heat and Power (CHP) for wind power integration in a district integrated heat and power system (DIHPS). However, to truly take advantage of these, many factors such as the energy price, the variable efficiency of devices, and especially the different heating regulation modes (the quantity regulation and the quality regulation mode) are imperative to be considered for variable situations of the practical project. Therefore, in this paper, an integrated hydraulic-thermal model of the district heating network (DHN) under the quantity regulation mode is introduced, and a novel thermal energy flow model with transmission time delay under the quality regulation mode is firstly proposed to simulate the dynamic thermal energy distribution of the DHN with multiple heat sources. Then, in terms of different heating regulation modes, the different thermal capacity of buildings to increase the flexibility of the DIHPS is studied. Moreover, an optimal scheduling method considering different energy stations and the thermal inertia of buildings under different heating regulation modes is proposed to guide the operation of the DIHPS in variable actual engineering applications for the sake of operational economics and wind power utilization. Finally, numerical cases have been compared based on a modified testing system. The results demonstrate that under the quantity regulation mode the coordination of energy stations effectively reduces the daily operation cost by 17.4%. The thermal inertia of buildings give a saving rate of 6.4% and he penalty cost of wind power is reduced by 36.3% further. Moreover, the total thermal capacity of buildings to reduce the operation cost and the wind power curtailment is less under the quality regulation mode. It results that the daily operation cost increases by 4.1%. In addition, towards the DIHPS with multiple energy stations in our case, the thermal inertia of pipes cloud not reduce the daily operation cost.

Nowadays, with the worldwide attention on renewable energy, the grid-connected photovoltaic (PV) equipment is playing a significant role in the active distribution network (ADN). This paper proposes a novel day-ahead optimal dispatch model for ADN based on the conditional value at risk (CVaR) to promote solar power accommodation. Firstly, the uncertainties of PV output and load are modeled by the generated typical scenarios based on historic data and K-means clustering and can be embedded in the discretized and linearized CVaR model. Then, the optimal dispatch model aimed to maximize solar power accommodation is established. Active management (AM) includes the on-load tap changer, capacitor bank, battery energy storage, and load curtailment. Constraints mainly involve power flow, nodal voltage, branch power load, and the limits of AM. The problem is finally transferred into a mixed-integer second-order cone programming one, which can be solved by the commercial solver Gurobi directly based on the branch-and-bound method. Case studies based on a modified IEEE 33-bus system illustrate the feasibility and validity of the proposed method.

Increasing penetration of DG brings many uncertain factors to the operation of the distribution network, which will increase difficulties for the recovery of power supply after the distribution network fault happens. Considering the real-time load demand and the random output changes of DG, this paper proposes a dynamic network reconfiguration method for distribution network fault recovery under high penetration of DG. First, a dynamic network reconfiguration model for distribution network fault recovery is established. Then, by introducing intermediate variables, a reasonable second-order cone relaxation is performed on the dynamic reconfiguration model. Third, the principle of dynamic islanding partition is introduced to coordinate with dynamic network reconfiguration. Finally, case studies are carried out on the IEEE 33-bus distribution network to illustrate the effectiveness of the proposed method.

With the increasing integration of renewable energy, the inertia of power systems evidently decreases and poses a critical challenge to planners. To guarantee the frequency stability of the system with high shares of renewable energy, this paper presents a novel multi-machine system frequency response (MSFR) model, which tends to additionally incorporate frequency responses from reheat generators and variable speed wind turbines (VSWTs) at the same time. After aggregating the multi-machine system into equivalent generators, the analytical trajectory of frequency nadir is deduced to construct linear frequency constraints through the fitting piecewise linear (FPWL) method. Then we propose a frequency-constrained stochastic generation planning model to satisfy the frequency nadir requirements. For the sake of online frequency support, the corresponding unit commitment is joined to flexibly adjust unit states. At last, the proposed model is applied to analyze the typical IEEE 24-bus test system. The derived simulation results have demonstrated the effectiveness of our model in terms of frequency deviations. Also, the benefits coming from different frequency control modes of VSWT-based wind farms are strongly revealed.

This paper proposes a novel energy bus-based automatic modeling method, which has the advantages of high extensibility and practicality. The proposed modeling method is applied to the energy station planning, which can optimize the capacity and inside topology simultaneously without any presupposition of the inside structure. Firstly, the traditional energy bus model is modified by introducing the distribution bus and defining the rules of energy flow. The concept of the digraph is utilized to model the energy station automatically, in which energy converters and connections are abstracted as nodes and edges. Then, the joint planning model considering the maintenance cost of the hardware-based real connections is constructed. Constraints mainly include energy conversion, power balance, inside topology, and the overall energy efficiency ratio (EER). The model is transformed into a mixed-integer linear program by the big M method and solved by Gurobi. Case studies based on a modified integrated energy system illustrate the validity of the proposed method. Results indicate the profound influences of the maintenance cost of real connections, energy price, and the overall EER limit on energy station planning.

The interconnection of sub-regions through tie lines can improve the overall economy of the system and provide a broad space for renewable energy consumption, such as wind power. However, at present, no detailed models exist for tie lines in the planning of multi-area power systems. In this paper, a novel multi-area and multi-objective model for the generation, transmission, and battery energy storage systems expansion planning is proposed in a central planning method that considers uncertainties of wind power and load. Tie lines among different subareas are used as support power sources, and detailed tie-line expansion planning models that consider the power flow direction, degree of power flow fluctuation, and number of power flow direction reversal in the tie line are presented to make the model more realistic. Besides, short-term operation problems (i.e., unit commitment) are incorporated in the long term planning issues to improve the adaptability of planning schemes. Linearized procedure for the different tie-line operation modes is implemented in order to improve the efficiency of the presented model. The proposed model is finally transformed into a mixedinteger linear programming formulation. The modified IEEE RTS-24 bus system is conducted to evaluate the practicality and effectiveness of the proposed method.

Fully electrification of ships brings great operating flexibility, which makes the shipboard microgrid can quickly respond to various navigation disturbances. In this paper, an adaptive voyage-generation scheduling in shipboard microgrid is proposed for addressing the time-varied weather conditions along the navigation route. The first stage is a pre-voyage planning and determines a tentative navigation route and cruising speed scheme with minimal fuel cost under pre-voyage forecasting weather conditions. The second stage is an on-line recourse based on intra-voyage forecasting weather conditions, which modifies the navigation route and cruising speed to keep away from the routes with adverse weather conditions and determines the onboard generation schemes. A 4-DG AES in a real-world weather routing scenario is set as the test case. Simulation results demonstrate the proposed method can well adapt to the time-varied weather conditions when navigation, and provide a safe and economic voyage-generation scheme.

This paper proposes a novel optimal configuration method of distributed generation and energy storage in microgrid. Firstly, K-means clustering method is introduced to formulate the typical days of the output power of renewable generation and load of the microgrid. Secondly, an optimal configuration model of distributed generation and energy storage in microgrid is established with the objective function of minimizing the annual total cost. Then, a hybrid solving strategy combing genetic algorithm, dynamic programming and primal-dual interior point method is proposed. Case studies are carried out on a real-world microgrid in China, and the results show the rationality of the model and the effectiveness of the solving algorithm.

This article proposes a novel optimal sizing method for the energy station in the multienergy system (MES) integrated with data center. First, an overall framework of the energy station, data center, and multienergy loads is constructed to describe the interconnection of energy system and information system. Then, the dynamic voltage frequency scaling (DVFS) technique, the thermal inertia, and the deep coupling relationships of the electricity load, waste heat, cooling load, and workload in the data center are respectively modeled. On this basis, the optimal sizing model of the energy station is established, in which the data center can participate in the integrated demand response by the DVFS technique and thermal inertia. The capacity of energy station and the scheduling schemes of energy devices and servers are optimized simultaneously to coordinate the energy flow and information flow. Based on the quasi-steady-state assumption of thermal balance, the model is simplified to a mixed-integer quadratic program problem, and it is solved by Gurobi. Case studies based on a real-world test system in China illustrate that the integration of data center can be utilized to reduce the total cost of energy station planning in MES.

With the increasingly integration of distributed generator (DG) in active distribution network (ADN), the fault location problem is facing more challenges. This paper proposes a hierarchical fault location model for ADN, in which the upper-layer model is to determine the area location of the fault and the lower-layer model is to get the section location of the fault. To solve the hierarchical fault location model in an efficient way, a hybrid solving strategy combing the genetic algorithm and the integer linear programming method is applied. Case studies are carried out on a modified IEEE 33-bus distribution network, in which the DGs are of high penetration. The results show that when a single or multiple fault occurs in the ADN, the hierarchical fault location method can detect the fault location more accurately than the traditional single-layer fault location method. And the results also indicate that the model and algorithm used in this paper perform significantly better than the single model and single algorithm in terms of convergence speed, accuracy, and fault tolerance.

Recently, the concept of integrated energy distribution system (IEDS) has gained popularity both in academia and industry, it is characterized by the comprehensive integration of multi-energy carriers and flexible utilization of available resources including distribution generations (DGs). However, the stability problem caused by the intermittency of renewable energy and uncertainties of energy demands are among the main concerns that need to be regulated properly. To address this issue, a novel method of tie-line power optimization is proposed in this paper, where the synergies among various energy sectors are stressed. The model of regional energy station (RES) is studied to provide the model basis, which can be regarded as the coupling link of electric power system (EPS) and natural gas system (NGS). In this regard, the flexibility of energy conversion and dispatch can be utilized as a kind of controllable resources for the sake of tie-line power optimization. Therefore, the tie-line power can be regulated in a flexible way in response to the control signal from the dispatch center. Verified by numerical studies, the results show the efficiency and applicability of the proposed strategy.

The dynamic behaviour of loads and operation strategy both have a great impact on the reliability evaluation of the integrated energy system (IES). This paper proposes a novel reliability evaluation method with the comprehensive consideration of the dynamic behavior and operation strategy. Firstly, the IES model based on the energy hub is presented. The dynamic behaviour is modeled in the form of differential equations, and the operation strategy is set by the difference between exergy. Then, the reliability evaluation method based on the performance of loads is proposed. The matrix of converter capacity and support capacity are defined to reflect the assistance process of multiple operation. The Markov chain Monte Carlo method is employed to calculate the reliability indexes in different scenarios. Finally, case studies are carried out on a practical user-level IES, and the results indicate that the multiple operation of subsystems in the IES can effectively increase the reliability.

This paper proposes a novel distributed generation (DG) planning methodology in active distribution network considering both demand side management and network reconfiguration. The objective function of the planning model is to minimize the total cost over the planning horizon, including investment cost of DG, operation and management cost of DG, fuel cost of DG, active management cost of DG, and demand side management cost. The constraints contain not only traditional DG investment and electrical restrictions (for instance, limitation of DG penetration, constraint of nodal voltage, constraint of branch capacity, etc.), but also the various restrictions of active management measures including regulating the on-load tap changer of transformer, controlling the output power of DG, demand side management and network reconfiguration. It is a large-scale mixed integer nonlinear programming model, which cannot be effectively solved by a single algorithm. Based on the idea of decomposition and coordination, the planning model is converted to a three-layer programming model. A hybrid solving strategy is developed to solve the model, in which differential evolution algorithm is used to determine the type, location and capacity of DG, and tree structure encoding-partheno genetic algorithm and primal–dual interior point method are applied to simulate the operation of active distribution network and find out the optimal operation state for each scenario. Case studies are carried out on a 61-bus active distribution network in East China, and results show that the total cost over the planning horizon can be reduced about 3.8% when demand side management and network reconfiguration are considered.

This paper proposes a novel multi-objective distributed generation planning methodology in distribution network considering correlations among uncertainties, i.e., wind speed, light intensity and load demand. First, under the framework of chance constrained programming, a multi-objective distributed generation planning model with the objective functions of minimizing both the annual total cost and the risk is established. The constraints of the model contain not only the restrictions of distributed generation investment and various electrical limitations, but also the restrictions of correlations among uncertainties. Second, an efficient solving strategy is employed to solve the planning model, in which the correlation-handled probabilistic power flow is used to deal with the correlated uncertainties, and non-dominated sorting genetic algorithm II is applied to achieve the Pareto optimal set of the model. Last, case studies are carried out on two test distribution networks, and the results show that a balance between the economy and the security can be achieved by non-dominated sorting genetic algorithm II. The case studies also verify that the correlations among uncertainties can influence the multi-objective distributed generation planning results, and the stronger the correlation is, the bigger the influence will be.

A peer-to-peer energy trading framework of buildings considering multi-energy coupling is presented in this study to improve energy efficiency and achieve cost savings. Microsources, such as photovoltaic and micro-combined heat and power, are used to meet electricity and heating demands, while the problem of unbalanced power is solved via the main grid and other peers through peer-to-peer energy trading. All peers aim to maximize their revenue and create trading strategies (i.e., trading period and volume) based on the time-of-use tariff released by the main grid. The trading model established by each peer is a parametric optimization-based problem because each peer must consider the trading willingness of other peers when formulating strategies. A new mixed-integer linear programming problem to maximize total profit can be finally formulated to obtain an equilibrium solution that satisfies all peers. Results show that the proposed peer-to-peer transaction approach can reduce energy bills of buildings. Daily purchased electricity from the main grid decreases by 8.9% through peer-to-peer energy trading between different buildings. In addition, compared with the building peer-to-peer transaction that only considers electricity energy management, the amount of purchased electricity from the main network can be reduced by 4.2% a day when multi-energy coupling is considered in peer-to-peer.

Nowadays, more and more wind turbine generators (WTGs) are being integrated into power systems. In this paper, an optimal WTG allocation model with the objective of minimizing the annual cost is proposed. The model can take into account the time-sequential correlation between the wind speed and load demand, as well as four kinds of active management measures, i.e. regulating the on-load tap changer of the transformer, curtailing the output power of WTG, regulating the power factor of WTG, and managing the demand side. The time-sequential correlation is handled by the joint probability distribution method. A hybrid solving strategy combining the fuzzy adaptive particle swarm optimization algorithm and primal-dual interior point method is developed to get the optimal solutions. Case studies carried out on the IEEE 33-bus active distribution network verify the feasibility of the developed model and the high efficiency of the proposed solving strategy. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

The benefits of transmission-distribution interactions at boundary buses are gradually unlocked in a restructured power network. Since the roles of transmission system planner and distribution system planner have some similarities in maintaining the economy and security of their systems, the transmission and distribution systems should collaborate and coordinate with each other to determine the expansion scheme in a positive and active manner. To address this problem, this paper presents a decomposition based expansion method for hybrid transmission and distribution networks (HTDNs), which simultaneously optimizes the hourly generation schedule. The original centralized optimization problem is decomposed into a transmission expansion subproblem encompassing numbers of distribution expansion subproblems. A distributed solution technique is proposed to guarantee shared variable consistencies. Case studies carried out on a HTDN, denoted as T24D9, validate the effectiveness and high performance of the formulated expansion problem.

With high-penetration renewable energy integrated, the absence of flexibility is endogenously appeared, which draws the importance of installing flexible devices. To improve the network flexibility and prevent stochastic contingencies, a novel security co-planning methodology of generation and transmission with multiple flexible devices is proposed in this paper. The flexible facility installation is composed of VSC-HVDC feeder construction and TCSC extension. The flexibility of transmission network is represented by load shedding and renewable curtailment. Compared with the previous research, the proposed novel model takes comprehensive consideration of both AC and DC losses, which is more suitable for the practical scenario. For the sake of contingency adaptability, N-1 stochastic contingency is taken into consideration by consecutive branch-breaking. Case studies are carried out on the modified Gaver 6-bus and IEEE 24-bus test systems, and the results show that the proposed model is able to enhance the flexibility, reduce the loss cost and guarantee the security of the power system.

This paper proposes a novel energy station capacity configuration method for residential district-level integrated energy system (DIES), which can take account into virtual energy storage (VES). Firstly, the residential DIES model is established based on the energy hub. The VES is analyzed through thermal balance by differential equations. Then the optimal capacity configuration model aimed at minimizing the total cost is presented. Constraints of energy purchase, power and energy balance, converters’ operation characteristics, reserve capacities and VES are all taken into consideration. Piecewise linearization is utilized to transfer the initial non-convex and non-linear problem into a mixed integer linear programming (MILP). A practical calculation example illustrates the validity of the method. Results indicate that compared to the traditional method, the capacity configuration considering VES can reduce the total cost by 5.23%.

Large-scale wind power is centralized access to the grid, which brings uncontrollable factors to the transmission network expansion planning (TNEP). The conventional deterministic planning model has scarce capacity to deal with uncertainties in the coming year. In this paper, uncertainties of load growth, generators output and wind power are modeled and a novel expected value model for TENP, based on probabilistic DC power flow, is proposed to minimize the annual comprehensive investment cost. For the sake of reducing the probability of line flow overload, the standard deviation is credited to the presented model’s constraint equations. In order to obtain the cumulants of wind power and load, the Latin hypercube sampling (LHS) technique is applied. Moreover, a dynamic adaptive multi-colony particle swarm optimization (DAMPSO) is designed to fix the empirical inertia weight in the algorithm. The test of the modified Garver-6 system indicates the rationality of the model and the validity of DAMPSO.

The application and integration of soft open point (SOP) can benefit the operation of active distribution network a lot. This paper proposes a multi-objective SOP planning methodology in active distribution network. The multi-objective SOP planning model is established based on the bi-level programming. The upper level model is solved by the nondominated sorting genetic algorithm-II. The lower level model is transferred to a second-order cone model by using convex relaxation, and it is solved by second-order cone programming method. The modified IEEE 33-bus active distribution network is used to test and validate the effectiveness of the proposed bilevel multi-objective SOP planning method.

A novel stochastic multi-area unit commitment (MAUC) framework is proposed to coordinate scheduling of generators and tie lines. In consideration of the randomness and volatility characteristics of wind energy, a worst-case based scenario selection method (SSM) based on the peak and valley shaving of the system, the ramping-up/down rates of net load, and the dispersion of uncertainty factors is presented to reduce the number of scenarios and improve the robustness of unit commitment (UC) schemes. Regarding the balance of efficiency and flexibility of tie-line scheduling, tie-line operation modes are established on the basis of the tie-line load rate (TLLR). Besides, the number of reversals of tie-line power flow directions during the entire scheduling period is also modeled. By linearizing the tie-line operation modes’ equations, the MAUC can be converted into a mixed-integer linear programming (MILP) formulation. Case studies show that more wind energy can be consumed by connecting power systems through tie lines and coordinating scheduling of generators and tie lines. Expected energy not supplied (EENS) of scenario reduction techniques based on the clustering method, i.e. k-means, Gaussian mixture model (GMM) and Fuzzy C-means (FCM), are 147%, 38%, and 130% higher than that of the SSM.