Search

• An investment scheme is proposed for a district heating system utilizing heat flexibility. • A new business model is proposed for a district heating system participating in energy and reserve markets. • The strategic behavior of district heating system is modeled as a bi-level optimization problem. • The flexibility region of the district heating system is modeled to ensure the feasibility of reserve deployment. The flexibility of district heating systems can be utilized to provide ancillary services for electric power systems and hence the investors can make profits by taking advantage of the arbitrage in multiple markets. This paper proposes a strategic investment scheme for a district heating system participating in both electricity energy and reserve markets, which enables the enhanced investment decisions for the installation of combined heat and power units, heat pumps, and thermal energy storage. A new business model is proposed for the district heating system that acts as a prosumer participating in energy and reserve markets, where the flexibility region of the district heating system is particularly modeled to ensure the feasibility of deployed reserves. The strategic behavior is modeled as a bi-level optimization problem. The upper-level problem determines the capacities and optimal offering/bidding strategies of district heating system, while the lower-level problem represents the energy and reserve market clearing over a set of representative days. The bi-level model is converted into a single-level mathematical program with equilibrium constraints using Karush-Kuhn-Tucker optimality conditions, and then reformulated as a mixed-integer linear problem by linearizing the nonlinear terms. Simulation results demonstrate that the total investment and operational costs of the district heating system are reduced by strategically offering in reserve market and the investor prefers to invest in thermal energy storage to shave peak heat load and combined heat and power units to increase the district heating system's flexibility other than heat pumps. The effectiveness of the flexibility model of the district heating system for feasible reserve deployment is also verified. [ABSTRACT FROM AUTHOR]

• A novel scenario generation method capturing extreme scenarios is proposed. • The correlation between energy prices is modelled by the Frank-Copula function. • A conditional-value-at-risk-based planning method is proposed. • The effectiveness and advantages of the proposed method are validated. The uncertainty and correlation of energy prices pose challenges to the planning and operation of park-level integrated energy systems. Especially, the extreme scenarios in both electricity and natural gas prices would result in significant economic losses, which have not been fully studied in the planning of park-level integrated energy systems. In this regard, this paper proposes a conditional-value-at-risk-based planning method for the park-level integrated energy system that considers both the extreme scenarios of energy prices and the correlation between the electricity price and natural gas price. First, the Gaussian mixture model is used to model the probability distribution function of the electricity price and natural gas price with high accuracy. Then, the Frank-Copula function is adopted to model the correlation between energy prices in different energy markets. Furthermore, a novel scenario generation method preserving extreme scenarios of energy prices is proposed, where extreme price scenarios are identified in the scenario generation process and subsequently preserved in scenario reduction. Finally, a conditional-value-at-risk-based planning model is proposed to optimize the overall investment and operation costs of the park-level integrated energy system, where the operational risks introduced by renewable generation and energy markets are effectively handled by the conditional value at risk. Case studies demonstrate that compared with the model without the consideration of extreme scenarios of energy prices, the potential risk loss of the proposed model is reduced by 3% in the same generated scenarios. The proposed model is more beneficial in enhancing the system's resilience to the risk. [ABSTRACT FROM AUTHOR]

• An enhanced wind power scenario generation and evaluation method is proposed. • A non-separable covariance function is incorporated to capture spatial and temporal correlations. • A more accurate parameter estimation method with fluctuation-based clustering is proposed. • New scenario evaluation indices from the perspective of system operators are introduced. Short-term wind power scenarios significantly affect the economic efficiency of the stochastic power system scheduling. In order to better capture the nonlinear spatio-temporal correlations of wind power, this paper proposes a scenario generation method integrated with non-separable spatio-temporal covariance function and fluctuation-based clustering for short-term wind power output. By taking advantage of well-calibrated marginal distribution modeled by Gaussian mixture model, the non-separable covariance function is incorporated in the scenario generation method to capture the complex interactions between spatial and temporal components of wind power. To estimate the covariance matrix more precisely, the historical data is grouped into K clusters with different fluctuations using the K-means clustering algorithm. Two indices are proposed to evaluate the scenarios in capturing the spatial and temporal correlations from the perspective of system operators. The proposed method is applied to a modified IEEE-118 system with four wind farms. Simulation results verify the superiority of the proposed method in capturing spatial and temporal correlations, and validate the economic benefits for the power system operation. [ABSTRACT FROM AUTHOR]

• Propose a SMPC based voltage regulation scheme to coordinate DG, OLTC, SVR and CBs considering uncertainty of DG outputs. • Use the time-correlated scenario generation method in the proposed SMPC framework. • Consider the single-phase regulation effect of OLTC, DGs and CBs. With the increasing penetration of renewable energy resources, the uncertainty of renewable energy resources output introduces challenges to voltage control in distribution networks (DNs). To solve these problems, this paper proposes a double-time-scale voltage control scheme for unbalanced DNs based on stochastic model predictive control (SMPC) to coordinate multiple voltage control devices. In fast-time-scale control, the active and reactive power outputs of distributed generators (DGs) are optimized to minimize voltage deviation, active power curtailment and reactive power fluctuation. In the slow-time-scale control, optimal tap changes of on-load tap changers (OLTC), step voltage regulators (SVR), and switched capacitor banks (CBs) are calculated to reduce voltage deviation and tap operations. Additionally, in the fast-time-scale control, the SMPC-based scheme adopts a scenario generation approach to represent the uncertainty of DG output. Considering the temporal correlation of the DG output, different prediction scenarios and scenario probabilities are obtained by the empirical cumulative distribution function fitted by the historical measurement data. In this paper, the effectiveness of the proposed double-time-scale voltage control scheme is verified by the unbalanced IEEE123 node system. The case study demonstrates that the proposed voltage scheme can effectively keep the three-phase voltages in unbalanced DN within the secure operation range and achieve better voltage profiles. [ABSTRACT FROM AUTHOR]

• A bi-level optimization method is proposed to coordinate the interests between the whole and individuals. • The carbon sources were analyzed from the perspective of virtual and actual carbon flow. • A refined accounting model considering both direct and indirect carbon emission is introduced. • A carbon trading method between microgrids is proposed to deal with carbon emissions. • A demand-side management framework is established. The multi-community integrated energy system (MCIES) has become a practical approach to improve energy efficiency and reduce carbon emissions with the promotion of urbanization. However, the current optimal scheduling of MCIES neither balances the total operation cost of the MCIES and the individual benefits of community microgrids effectively nor considers the carbon emission accounting and carbon trading of terminal integrated energy systems adequately. In this study, a bi-level optimal scheduling framework dominated by the regional dispatching center (RDC) and fully participated by the community integrated energy systems (CIESs) is developed for the scheduling of MCIES, which considers the energy interaction and carbon trading between CIESs. Firstly, a refined carbon emission accounting model considering direct and indirect carbon emissions is proposed, which can calculate the carbon emissions of each CIES in detail from the energy flow perspective based on the definition of virtual and actual carbon flow. Then, a one-master-multi-slave game model is established, where the RDC acts as the leader pursuing the minimum operation cost of the region by formulating energy interaction and carbon trading schemes between CIESs, while the CIESs are regarded as the followers that adjust the energy consumptions to minimize their operation costs. Finally, the effectiveness of the proposed optimization model is validated through the case studies, which are tested in a specific urban area. The simulation results demonstrate that the proposed optimal scheduling model can not only effectively coordinate the benefits between RDC and CIESs, but also reduce the overall operational cost and carbon emission of the MCIES. [ABSTRACT FROM AUTHOR]

• Propose a new real-time distributed voltage control scheme for MV&LV DNs based on ATC method. • Coordinate multiple DGs at different voltage levels to guarantee secure operation of the entire DN. • Design a dynamic allocation approach to determine the weighting factors for the optimization problem of MV&LV DNs. This paper proposes a new real-time distributed voltage control (RTDVC) scheme for medium and low voltage (MV&LV) distribution networks (DNs) based on the analytical target cascading (ATC) method. In this scheme, photovoltaic (PV) units at the LV level, on-load tap changer (OLTC) and distributed generation (DG) clusters at the MV level are optimally coordinated to regulate the bus voltages in MV&LV DNs to be close to the nominal value while minimizing the number of operations of OLTC and smoothing the reactive power outputs of DGs. Based on the ATC method, the large-scale strongly coupled voltage control problem of MV&LV DNs is decomposed into several subproblems while guaranteeing the optimality of the primal voltage optimization problem. The decomposed subproblems are iteratively solved in parallel in the MV and LV controllers, which achieves optimal voltage control in a distributed manner and effectively reduces the computation burden. An MV&LV test system with two 20 kV feeders and 39 0.4 kV LV grids was simulated to demonstrate the control performance of the proposed distributed voltage control scheme. [ABSTRACT FROM AUTHOR]

Highlights • Analyze the challenges for weakness analysis based on grid strength assessment in a realistic power grid. • Analyze the impact of power network structure on grid strength. • Propose a method for fast weakness analysis in a realistic power grid. • Verify the method in a realistic power grid. Abstract The increasing penetration of renewable energy resources (RERs) is challenging power grid planning and operation in maintaining grid reliability. Potential power grid reliability and stability issues may arise when large-scale RERs are connected to a weak power grid. These issues may become more significant due to the mutual interactions among RERs that are interconnected electrically close through a power network. Weakness analysis based on grid strength assessment is useful for identifying the potential weak grid issues. However, when taking into account the impact of the interactions among RERs, the weakness analysis becomes computationally challenging. Different combinations of points of interconnections (POIs) of RERs may have different impacts on grid strength at each POI. Due to the combination nature, such weakness analysis may be time-consuming when identifying the weakest combination of POIs from a large number of potential candidates in realistic power grids. To address this concern, the impact of the power network structure on grid strength is analyzed in this paper. It is found that the impact mainly depends on the impedance ratios at POIs. The findings can be summarized as two criteria to develop a recursive method for fast identification of the weakest combination in this paper. The efficacy of the proposed method is demonstrated through case studies on the IEEE 39-bus system and a realistic power grid. [ABSTRACT FROM AUTHOR]

The increasing displacement of synchronous generators with renewable resources such as wind and solar via power electronic interfaces causes a reduction in short-circuit strength and weak grid issues. The variation and uncertainty of renewable energy increase challenges for identifying weak grid conditions. This paper proposes an efficient method to analyze the impact of uncertain renewable energy on grid strength. The proposed method uses the probabilistic collocation method (PCM) to approximate the results of grid strength assessment under uncertain renewable generation, in order to reduce computational burden without compromising result accuracy when compared with traditional Monte Carlo simulation (MCS). To improve the accuracy of the approximation results, the proposed method integrates the K-means clustering technique with PCM to select the approximation samples of input variables. The efficacy of the proposed method is demonstrated by comparison with MCS on the modified IEEE 9-bus system and modified IEEE 39-bus system with multiple renewable generators. • Uncertainty in renewable generation increases complexities to identify weak grid issues • Quantification of impacts of renewable generation on grid strength using PCM • K-means clustering provides samples to enhance accuracy for grid strength analysis • ESDCSR-based grid strength assessment models impact of uncertainty probabilistically • Risk of weak grid increases with rise in uncertainty in renewable generation [ABSTRACT FROM AUTHOR]