High Renewable Power Systems Research Articles

Chance-Constrained Joint Dispatch of Generation and Wind Curtailment-Load Shedding Schemes With Large-Scale Wind Power Integration

Wind curtailment (WC) and load shedding (LS) are indispensable measures to mitigate the operational risk in high-renewable power systems. Moreover, WC and LS schemes should be pre-scheduled and confirmed by related entities to make them applicable. In this paper, we propose a novel chance-constrained economic dispatch (CCED) model which can generate optimal WC and LS schemes accounting for the reserve shortage and transmission congestion problems. In the proposed method, WC and LS power are formulated as random decision variables, with which the infeasibility issues of conventional CCED are fully addressed. To solve the proposed model, we first convert the complicated chance constraints into a set of deterministic inequalities equivalently by employing the conditional Value-at-Risk (CVaR) representation and duality theory. Then, a two-layer iterative algorithm is proposed to solve the equivalent problem efficiently, which is based on the generalized Benders decomposition (GBD) framework. Numerical tests demonstrate the effectiveness and efficiency of the proposed method.

Probabilistic Energy and Reserve Co-dispatch for High-renewable Power Systems and Its Convex Reformulation

Probabilistic Energy and Reserve Co-dispatch for High-renewable Power Systems and Its Convex Reformulation

Machine learning derived dynamic operating reserve requirements in high-renewable power systems

Accurately forecasting wind and solar power output poses challenges for deeply decarbonized electricity systems. Grid operators must commit resources to provide reserves to ensure reliable operations in the face of forecast errors, a process which can increase fuel consumption and emissions. We apply neural network-based machine learning to expand the usefulness of median point forecast data by creating probabilistic distributions of short-term uncertainty in demand, wind, and solar forecasts that adapt to prevailing grid conditions. Machine learning derived estimates of forecast errors compare favorably to estimates based on incumbent methods. Reserves derived from machine learning are usually smaller than values derived using incumbent methods, which enables fuel savings during most hours. Machine learning reserves are generally larger than incumbent reserves during times of higher forecast error, potentially improving system reliability. Performance is tested using multistage production simulation modeling of the California Independent System Operator system. Machine learning reserves provide production cost and greenhouse gas (GHG) emission reductions of approximately 0.3% relative to historical 2019 requirements. Savings in the 2030 timeframe are highly dependent on battery storage capacity. At lower levels of battery capacity, savings of 0.4% from machine learning reserves are shown. Significant quantities of battery storage are expected to be added to meet California's resource adequacy needs and GHG reduction targets. The addition of these batteries saturates reserve needs and results in minimal within-hour balancing costs in 2030.

Privacy-preserving mechanism for collaborative operation of high-renewable power systems and industrial energy hubs

Nowadays, achieving operational solutions to boost the flexibility of bulk power systems has become one of the major challenges in both industry and academia. The outcomes of recent studies demonstrate that the deployment of large-scale energy hubs can help enhance the flexibility of power systems. However, centralized management of networked energy hubs may not be compatible with the power system operator when they are managed by private owners. Motivated by this observation, a privacy-preserving decision-making structure is proposed in this paper for the collaborative operation of private industrial energy hubs and renewable power system by considering the high penetration of renewable energy sources. The proposed structure is drawn up based on the decentralized two-stage robust–stochastic approach and solved using the Benders decomposition algorithm by relying on the private ownership of various entities. The main objectives of this study lie in (1) decreasing renewable power curtailment and (2) minimizing the total operation costs of the private entities. To achieve these objectives, the effects of the multi-energy demand response program and energy conversion facilities are investigated in the context of the developed model. The competency and robustness of the proposed collaborative decision-making structure are examined on the IEEE 30-bus test system using GAMS and DIgSILENT PowerFactory software. Results show that if industrial energy hubs are successfully deployed in industrial parks, the total operation cost of the renewable power system decreases by up to 16.33%, renewable power curtailment reduces by 92.9%, and flexibility of the renewable power system enhances by increasing spinning reserve.

Comparing negative emissions and high renewable scenarios for the European power system

Emerging literature highlights the essential role played by decarbonised electricity generation in future energy systems consistent with the Paris Climate Agreement. This analysis compares the impacts of high levels of renewable electricity and negative emissions technologies on exploratory visions of the future EU power system (2050) in terms of emissions reduction, technical operation and total system costs. The analysis shows that high renewable power system scenarios coupled with low levels of negative emissions technologies, such as biomass carbon capture and storage (< 2% of installed capacity), can deliver a net-negative European power system at lower comparable cost without breaching published sustainable biomass potentials in Europe (or requiring imports) or geological storage potentials while also contributing to power system inertia. Direct air capture has the capability to further decrease overall carbon emissions. While carbon capture and storage and power-to-gas must overcome market, regulatory and social acceptance challenges, given their potential benefits to emissions reduction, costs and system operation their role in a future power system should be further explored.

Flexible nuclear co-generation plant combined with district heating and a large-scale heat storage

The paper presents a concept of flexible nuclear co-generation. A plant operating according to the concept can vary its production of heat and electricity by utilising steam on different pressure levels. It is also connected to a district heating system and utilises a large-scale heat storage.France has set long-term targets to increase the share of solar and wind power in the power mix, consequently reducing the share of nuclear power. The flexible co-generation can help the nuclear fleet cope with this change while potentially reducing emissions from the heating sector. High utilisation rate, efficiency and more stable reactor output is targeted.The concept is studied with an optimisation model within the French operational environmental in three case studies of Paris, Lyon and Dunkirk. Total efficiencies of 70%, 54% and 34% are reached, respectively. Cases represent systems with different heat loads and distances from nearby DH systems. Result indicate that both operational flexibility and large-scale storages are heavily utilised. However, compared to the flexibility required by a high-renewable power system, the plants can only play a small part. Also, the current district heating systems need development to utilise a large-scale heat source such as nuclear co-generation.

Scenario-Based Economic Dispatch With Tunable Risk Levels in High-Renewable Power Systems

This paper introduces an empirical approach to dispatch resources in real-time power system operation with growing levels of uncertainties emerging from intermittent and distributed energy resources in the supply and the demand side. It is shown that by taking empirical data of specific sizes, the dispatch results can lead to a quantifiable and rigorous bound on the risk of violating constraints at the implementation stage. In particular, we formulate the look-ahead real-time economic dispatch problem using the scenario approach. This approach takes empirical data as input and guarantees a tunable probability of violating the constraints according to the input data size. By exploiting the structure of the economic dispatch, we show that in the absence of transmission constraints, the number of samples that is required by the theory does not grow with the size of the problem. In the more general case with transmission constraints, it is shown that the posterior bound on the risk of dispatch can be quantified and can be much smaller than the risk bound before solving the dispatch. Numerical examples based on a standard test system suggest that the scenario approach can provide a practically attractive solution with theoretically rigorous properties for risk-limiting power system operations.

Intercomparison between Switch 2.0 and GE MAPS models for simulation of high-renewable power systems in Hawaii

BackgroundNew open-source electric-grid planning models have the potential to improve power system planning and bring a wider range of stakeholders into the planning process for next-generation, high-renewable power systems. However, it has not yet been established whether open-source models perform similarly to the more established commercial models for power system analysis. This reduces their credibility and attractiveness to stakeholders, postponing the benefits they could offer. In this paper, we report the first model intercomparison between an open-source power system model and an established commercial production cost model.ResultsWe compare the open-source Switch 2.0 to GE Energy Consulting’s Multi-Area Production Simulation (MAPS) for production-cost modeling, considering hourly operation under 17 scenarios of renewable energy adoption in Hawaii. We find that after configuring Switch with similar inputs to MAPS, the two models agree closely on hourly and annual production from all power sources. Comparing production gave a coefficient of determination of 0.996 across all energy sources and scenarios, indicating that the two models agree on 99.6% of the variation. For individual energy sources, the coefficient of determination was 69–100.ConclusionsAlthough some disagreement remains between the two models, this work indicates that Switch is a viable choice for renewable integration modeling, at least for the small power systems considered here.

Non-Invasive Identification of Inertia Distribution Change in High Renewable Systems Using Distribution Level PMU

This letter proposed an approach to identify the change of inertia distribution in high renewable power systems. Using the footprints of electromechanical wave propagation at the distribution level, this approach provides a new and non-invasive way to aware the system inertia distribution for primary frequency response. Actual measurements and high renewable dynamic models validated effectiveness of the approach.

Research and deployment priorities for renewable technologies: Quantifying the importance of various renewable technologies for low cost, high renewable electricity systems in an Australian case study

This study aims to identify research priorities to enable low cost, high renewable power systems. An evolutionary program optimises the mix of technologies in 100% renewable energy portfolios (RE) in the Australian National Electricity Market. Various technologies are reduced in availability to determine their relative importance for achieving low costs. The single most important factor is found to be the integration of large quantities of wind; therefore wind integration is identified as a research priority. In contrast, photovoltaics are found to “saturate” the system at less than 10% of total energy (in the absence of storage or demand management, installation of further photovoltaics does not contribute significant further value). This indicates that policies to promote utility-scale photovoltaics should be considered in partnership with complementary measures (such as demand side participation and storage). Biofuelled gas turbines are found to be important; a complete absence of bioenergy increases costs by AU$20–30/MWh, and even having only 0.1TWh per year of bioenergy available reduces average costs by AU$3–4/MWh. Limits on the non-synchronous penetration (NSP) are found to be relatively expensive, suggesting a significant research priority around finding alternative approaches to providing synchronous services, such as inertia. Geothermal and concentrating solar thermal technologies do not appear essential as long as sufficient wind and peaking bioenergy is available.