Benefits of demand-side response in providing frequency response service in the future GB power system

The demand for ancillary service is expected to increase significantly in the future Great Britain (GB) electricity system due to high penetration of wind. In particular, the need for frequency response, required to deal with sudden frequency drops following a loss of generator, will increase because of the limited inertia capability of wind plants. This paper quantifies the requirements for primary frequency response and analyses the benefits of frequency response provision from demand-side response (DSR). The results show dramatic changes in frequency response requirements driven by high penetration of wind. Case studies carried out by using an advanced stochastic generation scheduling model suggest that the provision of frequency response from DSR could greatly reduce the system operation cost, wind curtailment, and carbon emissions in the future GB system characterized by high penetration of wind. Furthermore, the results demonstrate that the benefit of DSR shows significant diurnal and seasonal variation, whereas an even more rapid (instant) delivery of frequency response from DSR could provide significant additional value. Our studies also indicate that the competing technologies to DSR, namely battery storage, and more flexible generation could potentially reduce its value by up to 35%, still leaving significant room to deploy DSR as frequency response provider

Transmission network expansion planning with stochastic multivariate load and wind modeling

The increasing penetration of intermittent energy sources along with the introduction of shiftable load elements renders transmission network expansion planning (TNEP) a challenging task. In particular, the ever-expanding spectrum of possible operating points necessitates the consideration of a very large number of scenarios within a cost-benefit framework, leading to computational issues. On the other hand, failure to adequately capture the behavior of stochastic parameters can lead to inefficient expansion plans. This paper proposes a novel TNEP framework that accommodates multiple sources of operational stochasticity. Inter-spatial dependencies between loads in various locations and intermittent generation units' output are captured by using a multivariate Gaussian copula. This statistical model forms the basis of a Monte Carlo analysis framework for exploring the uncertainty state-space. Benders decomposition is applied to efficiently split the investment and operation problems. The advantages of the proposed model are demonstrated through a case study on the IEEE 118-bus system. By evaluating the confidence interval of the optimality gap, the advantages of the proposed approach over conventional techniques are clearly demonstrated

Techno-economic assessment of battery storage and Power-to-Gas: A whole-system approach

The power systems in many countries are undergoing a radical transformation through employing a large capacity of renewable generation technologies such as wind turbine and solar photovoltaic. The power generation by wind and solar resources are variable and difficult to predict. Therefore, growing capacities of such technologies is expected to introduce challenges regarding balancing electricity supply and demand. This paper investigates the role of battery storage and power-to-gas systems to accommodate large capacity of intermittent power generation from wind and solar and therefore facilitates matching electricity supply and demand. The Combined Gas and Electricity Networks (CGEN) model was used to optimize the operation of gas and electricity networks in GB for typical weeks in winter and summer in 2030. The role of different capacity of battery storage and power-to-gas systems in reducing the wind curtailment and operating cost of the system were quantified and compared with the annualized cost of these technologies

C-Vine copula mixture model for clustering of residential electrical load pattern data

The ongoing deployment of residential smart meters in numerous jurisdictions has led to an influx of electricity consumption data. This information presents a valuable opportunity to suppliers for better understanding their customer base and designing more effective tariff structures. In the past, various clustering methods have been proposed for meaningful customer partitioning. This paper presents a novel finite mixture modeling framework based on C-vine copulas (CVMM) for carrying out consumer categorization. The superiority of the proposed framework lies in the great flexibility of pair copulas towards identifying multi-dimensional dependency structures present in load profiling data. CVMM is compared to other classical methods by using real demand measurements recorded across 2,613 households in a London smart-metering trial. The superior performance of the proposed approach is demonstrated by analyzing four validity indicators. In addition, a decision tree classification module for partitioning new consumers is developed and the improved predictive performance of CVMM compared to existing methods is highlighted. Further case studies are carried out based on different loading conditions and different sets of large numbers of households to demonstrate the advantages and to test the scalability of the proposed method

Coordinated operation of gas and electricity systems for flexibility study

The increase interdependencies between electricity and gas systems, driven by gas-fired power plants and gas electricity-driven compressors, necessitates detailed investigation of such interdependencies, especially in the context of increased share of renewable energy sources. 6 In this paper, the value of an integrated approach for operating gas and electricity systems is assessed. An outer approximation with equality relaxation (OA/ER) method is used to deal with the optimization class of mixed integer non-linear problem of integrated operation of gas and electricity systems. This method significantly improved the efficiency of the solution algorithm and achieved nearly 40% reduction in computation time compared to successive linear programming. The value of flexibility technologies including flexible gas compressors, demand side response, battery storage, and power-to-gas is quantified in the operation of integrated gas and electricity systems in GB 2030 energy scenarios for different renewable generation penetration levels. The modeling demonstrates that the flexibility options will enable significant cost savings in the annual operational costs of gas and electricity systems (up to 21%). On the other hand, the analysis carried out indicates that deployment of flexibility technologies support appropriately the interaction between gas and electricity systems

Stochastic optimization model for coordinated operation of natural gas and electricity networks

Renewable energy sources will anticipate significantly in the future energy system paradigm due to their low cost of operation and low pollution. Considering the renewable generation (e.g., wind) intermittency, flexible gas-fired power plants will continue to play their essential role as the main linkage of natural gas and electricity networks, and hence coordinated operation of these networks is beneficial. Furthermore, uncertainty is always found in gas demand prediction, electricity demand prediction, and output power of wind generation. Therefore, in this paper, a two-stage stochastic model for operation of natural gas and electricity networks is implemented. In order to model uncertainty in these networks, Monte Carlo simulation is applied to generate scenarios representing the uncertain parameters. Afterwards, a scenario reduction algorithm based on distances between the scenarios is applied. Stochastic and deterministic models for natural gas and electricity networks are optimized and compared considering integrated and iterative operation strategies. Furthermore, the value of flexibility options (i.e., electricity storage systems) in dealing with uncertainty is quantified. A case study is presented based on a high pressure 15-node gas system and the IEEE 24-bus reliability test system to validate the applicability of the proposed approach. The results demonstrate that applying the stochastic model of gas and electricity networks as well as considering integrated operation strategy in the presence of flexibility provides different benefits (e.g., 14% cost savings) and enhances the system reliability in the case of contingency

Investing in flexibility in an integrated planning of natural gas and power systems

The growing interdependencies between natural gas and power systems, driven by gas-fired generators and gas compressors supplied by electricity, necessitates detailed investigation of the interactions between these vectors, particularly in the context of growing penetration of renewable energy sources. In this research, an expansion planning model for integrated natural gas and power systems is proposed. The model investigates optimal investment in flexibility options such as battery storage, demand side response, and gas-fired generators. The value of these flexibility options is quantified for gas and electricity systems in GB in 2030. The results indicate that the flexibility options could play an important role in meeting the emission targets in the future. However, the investment costs of these options highly impact the future generation mix as well as the type of reinforcements in the natural gas system infrastructure. Through deployment of the flexibility options up to £24.2b annual cost savings in planning and operation of natural gas and power systems could be achieved, compared to the case that no flexibility option is considered