Water-Energy Nexus Management for Power Systems

The water system management problem has been widely investigated. However, the interdependencies between water and energy systems are significant and the effective co-optimization is required considering strong interconnections. This paper proposes a two-stage distributionally robust operation model for integrated water-energy nexus systems including power, gas and water systems networked with energy hub systems at a distribution level considering wind uncertainty. The presence of wind power uncertainty inevitably leads to risks in the optimization model. Accordingly, a coherent risk measure, i.e., conditional value-at-risk, is combined with the optimization objective to determine risk-averse operation schemes. This two-stage mean-risk distributionally robust optimization is solved by Bender's decomposition method. Both the day-ahead and real-time operation cost are minimized with an optimal set of scheduling the multi-energy infrastructures. Case studies focus on investigating the strong interdependencies among the four interconnected energy systems. Numerical results validate the economic effectiveness of IES through optimally coordinating the multi-energy infrastructures. The proposed model can provide system operators a powerful two-stage operation scheme to minimise operation cost under water-energy nexus considering risk caused by renewable uncertainties, thus benefiting customers with lower utility bills

Coordinated Risk Mitigation Strategy for Integrated Energy Systems under Cyber-Attacks

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Long-Run Network Pricing for Security of Supply in Distribution Networks

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Location of low-frequency oscillation sources using improved D-S evidence theory

This paper presents a method for localizing oscillation sources based on data fusion Dempster–Shafer (D-S) evidence theory. This study is based on data of each bus from the system collected by phasor measurement units (PMUs). Then the D-S evidence theory algorithm is employed to establish the mass function and the trust degree of each bus. Three traditional methods are used to locate the oscillation source and to provide the calculation results for structuring the mass function of the algorithm. Finally, the decision of oscillation sources localization is made according to synthesis decision value. The higher the synthesis decision value, the higher the possibility is of an oscillation source. The WECC179 bus power system is applied for verification, and the D-S evidence theory method is compared with the above traditional three methods. It is proved that the algorithm can significantly improve the accuracy of locating sources of negatively damped oscillations and forced power oscillations, effectively reduce misjudgment. Meanwhile, this algorithm is also effective for the positioning complex dual oscillation sources.</p

Adaptive energy management for hybrid power system considering fuel economy and battery longevity

The adoption of hybrid powertrain technology brings a bright prospective to improve the economy and environmental friendliness of traditional oil-fueled automotive and solve the range anxiety problem of battery electric vehicle. However, the concern of the battery aging cost is the main reason that keeps plug-in hybrid electric vehicles (PHEV) from being popular. To improve the total economy of PHEV, this paper proposes a win-win energy management strategy (EMS) for Engine-Battery-Supercapacitor hybrid powertrains to reduce energy consumption and battery degradation cost at the same time. First of all, a novel hierarchical optimization energy management framework is developed, where the power of internal combustion engine (ICE), battery and super capacitor (SC) can be gradationally scheduled. Then, an adaptive constraint updating rule is developed to improve vehicle efficiency and mitigate battery aging costs. Additionally, a control-oriented cost analyzing model is established to evaluate the total economy of PHEV. The quantified operation cost is further designed as a feedback signal to improve the performance of the power distribution algorithm. The performance of the proposed method is verified by Hardware-in-the-loop experiment. The results indicate that the developed EMS method coordinates the operation of ICE, driving motor (DM) and energy storage system effectively with the fuel cost and battery aging cost reduced by 6.1% and 28.6% respectively compared to traditional PHEV. Overall, the introduction of SC and the hierarchical energy management strategy improve the total economy of PHEV effectively. The results from this paper justify the effectiveness and economic performance of the proposed method as compared to conventional ones, which will further encourage the promotion of PHEVs.</p

Reliability-based Probabilistic Network Pricing with Demand Uncertainty

The future energy system embraces growing flexible demand and generation, which bring large-scale uncertainties and challenges to current deterministic network pricing methods. This paper proposes a novel reliability-based probabilistic network pricing method considering demand uncertainty. Network reliability performance, including probabilistic contingency power flow (PCPF) and tolerance loss of load (TLoL), are used to assess the impact of demand uncertainty on actual network investment cost, where PCPF is formulated by the combined cumulant and series expansion. The tail value at risk (TVaR) is used to generate analytical solutions to determine network reinforcement horizons. Then, final network charges are calculated based on the core of the Long-run incremental cost (LRIC) algorithm. A 15-bus system is employed to demonstrate the proposed method. Results indicate that the pricing signal is sensitive to both demand uncertainty and network reliability, incentivising demand to reduce uncertainties. This is the first-ever network pricing method that determines network investment costs considering both supply reliability and demand uncertainties. It can guide better sitting and sizing of future flexible demand in distribution systems to minimise investment costs and reduce network charges, thus enabling a more efficient system planning and cheaper integration.</p

Small signal stability analysis for different types of PMSGs connected to the grid

Small signal stability of permanent magnet synchronous generator (PMSG)-based wind turbines connected to the power grid should be studied properly in order to facilitate damping strategy design. In this paper, unified small-signal models for different types of PMSGs are developed to study their small-signal stability. The models are composed of mechanical systems, electrical systems and control systems. A two-mass shaft model for the mechanical system is provided to analyze the dynamic and steady-state behaviors of the wind turbine and generator rotor. Meanwhile, PMSG, converter system and transmission line are separately modeled to build unified small-signal models for three PMSG-based wind turbine generator systems (WTGS). Then, based on unified small-signal models, eigenvalue analysis is conducted to determine the relation between different oscillation modes and state variables through calculating participation factors. With modal analysis, the developed small signal models are able to find out all types of oscillation modes for PMSGs connected to the power grid, which are subsynchronous oscillation (SSO), subsynchronous control interaction (SSCI) and low-frequency oscillation, including frequency and damping of each oscillation mode. Different initial values of the small signal models can influence both frequencies and damping ratios of oscillation modes, which lay basis for further damping strategy study.</p

Impact Analysis of Seismic Events On Integrated Electricity and Natural Gas Systems

Seismic events can cause devastating impacts on both overground and underground energy system infrastructure. This paper proposes a methodology to evaluate the impact of seismic events on the security of integrated electricity and gas system, mainly focusing on pipelines leakage and connection loss of electricity transmission lines. A stochastic model is used to formulate the damage level based on earthquake severity. The seismic impact on the integrated system is classified according to the levels of pipe leak and electricity line failure. Load curtailment due to limited generation capacity and overloaded transmission lines is thereafter quantified. Seismic intensity is generated randomly based on Monte Carlo simulation so that a certain seismic intensity can be related to relevant load curtailment. An integrated energy system with a 30-busbar electricity system and a 6-node natural gas network is used to demonstrate the effectiveness of the proposed method. The results clearly illustrate damage consequences under seismic events in terms of both probability and severity levels. This work can inform resilience enhancement scheme design based on the vulnerability performance and impact of both systems