The emerging prosumers have changed the way traditional distribution systems operate. While harvesting the benefits of Distributed Energy Resources (DERs), prosumers' self-dispatching and internal ...energy trading can also bring technical challenges and economic loss to the distribution system operator (DSO). To this end, a DSO-prosumers cooperated scheduling framework for the transactive distribution system, which considers the impact of prosumers' peer-to-peer (P2P) energy trading, is proposed in this paper. First, by incorporating the P2P energy trading among prosumers into scheduling, prosumers and the DSO can cooperate to optimize the power flow. Meanwhile, the Nash bargaining theory is introduced to achieve the optimal allocation of interests. Then, to improve the computational efficiency, the established bargaining problem is decomposed into two subproblems, i.e., the optimal physical power flow problem, and the virtual power flow bargaining problem. Finally, in order to protect individual privacy, each problem is solved via the alternating direction method of multipliers (ADMM) algorithm in a distributed manner. Case study based on the IEEE 33-bus test system verifies the effectiveness of the proposed method.
•A reserve coordination strategy is proposed to exploit multiple resources coping with uncertain power.•An ADN scheduling framework with feedback mechanism is presented.•A multi-timescale scheduling ...method considering temporal-spatial reserve coordination is formulated.
To improve the self-adjusting ability of the distribution network and reduce the impact of fluctuated power on the upstream transmission network, a multi-timescale active distribution network (ADN) scheduling method, which includes a reserve coordination strategy and a scheduling framework, is proposed in this paper. The reserve coordination strategy can schedule available reserve resources according to their temporal-spatial characteristics, and the scheduling framework further uses the reserve margin as feedback signals and optimizes resources in both the transmission and distribution network under different timescale. In the day-ahead stage, reserve provided by transmission network and operation of slow-response resources are optimized based on the day-ahead forecast. In the intraday stage, the reserve and output of remaining distributed energy resources (DERs) in the distribution network are updated according to the renewed forecast with more accuracy. In the real-time stage, combining with the model predictive control (MPC), deviations from the dispatched results are minimized to adjust the output of DERs on a rolling basis. The temporal reserve coordination for day-ahead and intraday can reduce the real-time deviation, and the spatial reserve coordination for transmission and distribution network can minimize the impact of power fluctuations on the upstream transmission. Case study on an IEEE 33-bus system verified the effectiveness of the proposed method.
A large number of renewable energy resources are integrated into the integrated energy system (IES), which complicates the IES dispatching, especially for accommodating anti-peak-regulation of wind ...power. To cope with that, a day-ahead IES optimal dispatching method considering power to gas (P2G) units and dynamic pipeline networks is proposed in this article. First, by introducing P2G, an IES structure based on energy hub is established to implement bidirectional flow between power and natural gas systems. Second, the dynamic characteristic of gas pipelines is modeled with energy storage capability, which can improve the flexibility of the natural gas system by regulating the pressure level of pipeline networks. Furthermore, a cooperative dispatching strategy for P2G and pipeline storage capability is presented to catch the flexibility of IES, in which the unbalanced wind power is converted into natural gas and stored in pipeline networks. Finally, case study is verified on the modified IEEE39-NGS20-HS20 and IEEE118-NGS40-HS20 IES systems with different typical wind power scenarios. The proposed cooperative dispatching strategy can effectively increase wind power consumption and reduce operating cost of the whole system without high computation burden.
A large number of distributed energy resources (DERs) integrate into the distribution network, which changes the power flow, increases the power fluctuations, and complicates the scheduling of the ...distribution network. To cope with that, a multitimescale scheduling method, which considers the demand response as well as user satisfaction, is proposed in this paper. First, in the day-ahead stage, both the generation-side and demand-side are combined to minimize the operating costs and reduce the impact of DERs. Second, in the real-time stage, the model predictive control method is introduced, smoothing the power fluctuations and maximizing the consumed renewable energy. Finally, the user comprehensive satisfaction is considered, ensuring the users' benefit and improving the flexibility of users to participate in scheduling while shifting electricity demands. By optimizing the generation-side and demand-side on both day-ahead and real-time timescales, the proposed method can improve the operation status for the distribution network effectively while ensuring the interests of users. Simulation on an improved IEEE-33 bus distribution system verifies the effectiveness of the proposed method.
Energy storage including electric, gas and thermal storages, is considered as suitable equipment to improve the flexibility of integrated energy system (IES). However, the complex interaction among ...different energy makes IES planning difficult when the stochastic wind power is integrated. In this paper, an optimal planning method of multi-type energy storage in IES integrated uncertain wind power is proposed First, the scenario method is used to express the influence of wind power uncertainty. Second, multi-energy storage planning model is established to reflect the interaction between each energy system, and improve the flexibility of IES. The total cost containing investment, operation, loss of load and wind power curtailment costs, is minimized as the planning objective Finally, the test IES is built which coupled with IEEE-14 power, NGS-14 gas, and TS-14 thermal systems. The results demonstrate the validity and correctness of the proposed method.
A large number of distributed generators (DGs) integrates into the distribution network, which changes the power flow, increases the power fluctuations and complicates the scheduling of the ...distribution network. To cope with that, a multi-timescale scheduling method, which considers the demand side response as well as user satisfaction, is proposed in this paper. Firstly, in the day-ahead stage, both the generation side and demand side are incorporated to minimize the operating costs and reduce the impact of DGs. Secondly, in the real-time stage, the model predictive control (MPC) method is introduced, smoothing the power fluctuations and maximizing the consumed renewable energy. Finally, the user comprehensive satisfaction is considered, which can ensure the users' benefit when shifting the demand and improve the flexibility of the user to participate in scheduling. Simulation on an improved IEEE-33 bus distribution system verifies the effectiveness of the proposed method.
To improve the adaptability for renewable energy sources (RES), a multi-time scale optimal dispatching strategy based on the scenario method is proposed in the active distribution network (ADN). In ...the day-ahead stage, polynomial normal transformation and Latin hypercube sampling technology are employed to generate scenarios with temporal correlation. By optimizing the operation state of slow-response resources, the expected cost of ADN under all scenarios achieves a minimum. In the intraday stage, based on the updated predicted output of RES, the operation status of the fast-response resources is optimized using the same procedure. In the real-time stage, a rolling finite time-domain optimization strategy is adopted to minimize the deviation between the actual output and the intraday reference. Through the coordination of different time scales, the proposed method can reduce the action numbers of discrete reactive power compensation devices and restrain the fluctuation of bus voltage. A case study on a modified IEEE 33-bus system verified the economy and effectiveness of the proposed method.
A large number of distributed generators (DG) has been integrated into the distribution network, which has changed its flow and brought challenges to the existing dispatch methods. To cope with the ...fluctuating power caused by these DG in the distribution network, a multi-time scale active and reactive power coordinated dispatch method is proposed in this paper. Firstly, based on the prediction of renewable energy output in the next 24 hours, the operational output of DGs and energy storage (ES) is optimized in the day-ahead stage. Then, combining the latest prediction data and the reference value from the day-ahead stage, the intra-day stage is conducted every 4 hours. Thus, the operational cost is minimized, and the power fluctuation caused by DG to the main grid is reduced. Finally, simulation results verified the effectiveness of this method.
Aiming at the large-scale random power supply being integrated into the power grid, this paper builds an adaptive distributionally robust unit commitment model (ADRUC) to solve the optimal scheduling ...problem of the power system under uncertain operating conditions. First, the ambiguity set is built based on confidence bands for cumulative distribution function (CDF) from non-parametric statistics, and the interval of acceptable wind power output is obtained, then a polyhedral uncertainty set is constructed considering both the range and temporal domains. Then, in view of the flexibility and economy of fully adjustable robust optimization (FARO), the primal problem is divided into day-ahead UC master problem and the sub-problem of economic dispatch under the worst-case scenario. The strong duality theorem and the Big-M method are employed to transform the sub-problem into a MILP problem, the column and constraint generation (C & CG) algorithm is iterated repeatedly to obtain the target solution. Finally, the ADUC model is analyzed and validated by a ten-machine system.
To address the increase of wind power uncertain along with wind power capacity, a bi-level robust unit commitment (RUC) model is built to obtain the optimal unit commitment scheme in the worst-case ...scenario. The objective function considers the start-up/off cost and operation cost of all units. Considering the temporal correlation characteristics of wind power forecast errors, a polyhedral uncertainty set is designed. Conditional Value-at Risk (CVaR) is adopted to describe the risk loss when the real wind power output is beyond the predefined uncertainty set and also determine the interval of acceptable wind power output. In view of the inner and outer layers of model interact with each other, the primal problem is decomposed into day-ahead UC master problem and the sub-problem which considers economic dispatch. In the solving process, the strong duality theorem and the Big-M method are employed to transform the sub-problem with max-min structure into a MILP problem, then the problem is solved by the column and constraint generation (C&CG) algorithm. Finally, the results show the effectiveness of the proposed model.
