High Penetration Of Renewable Generation Research Articles

Whole-System Assessment of the Benefits of Integrated Electricity and Heat System

The interaction between electricity and heat systems will play an important role in facilitating the cost effective transition to a low carbon energy system with high penetration of renewable generation. This paper presents a novel integrated electricity and heat system model in which, for the first time, operation and investment timescales are considered while covering both the local district and national level infrastructures. This model is applied to optimize decarbonization strategies of the U.K. integrated electricity and heat system, while quantifying the benefits of the interactions across the whole multi-energy system and revealing the trade-offs between portfolios of: 1) low carbon generation technologies (renewable energy, nuclear, and CCS) and 2) district heating systems based on heat networks and distributed heating based on end-use heating technologies. Overall, the proposed modeling demonstrates that the integration of the heat and electricity system (when compared with the decoupled approach) can bring significant benefits by increasing the investment in the heating infrastructure in order to enhance the system flexibility that in turn can deliver larger cost savings in the electricity system, thus meeting the carbon target at a lower whole-system cost.

Electric Vehicle Charging Strategy for Isolated Systems with High Penetration of Renewable Generation

Inhabited islands depend primarily on fossil fuels for electricity generation and they also present frequently a vehicle fleet, which result in a significant environmental problem. To address this, several governments are investing in the integration of Renewable Energy Sources (RESs) and Electric Vehicles (EVs), but the combined integration of them creates challenges to the operation of these isolated grid systems. Thus, the aim of this paper is to propose an Electric Vehicle charging strategy considering high penetration of RES. The methodology proposes taxing CO 2 emissions based on high pricing when the electricity is mostly generated by fossil fuels, and low pricing when there is a RES power excess. The Smart charging methodology for EV optimizes the total costs. Nine scenarios with different installed capacity of solar and wind power generation are evaluated and compared to cases of uncoordinated charging. The methodology was simulated in the Galapagos Islands, which is an archipelago of Ecuador, and recognized by the United Nations Educational, Scientific and Cultural Organization (UNESCO) as both a World Heritage site and a biosphere reserve. Simulations results demonstrate that the EV aggregator could reduce costs: 7.9% for a case of 5 MW installed capacity (wind and PV each), and 7% for a case of 10 MW installed (wind and PV each). Moreover, the use of excess of RES power for EV charging will considerably reduce CO 2 emissions.

Consensus-Based Droop Control of Isolated Micro-Grids by ADMM Implementations

Due to low inertia, high penetration of renewable generators in isolated micro-grids may result in poor frequency and voltage response under large disturbances. In order to overcome this difficulty, the distributed average consensus algorithm has been studied recently by extending local droop control mechanisms of power converters into distributed droop control. However, its convergence rate and the resilience to noises are still not satisfactory. In this paper, two implementations of consensus-based distributed droop control by the alternating direction multipliers method are proposed. It can be shown that the closed-loop system is described by the second-order vector differential equation, and the stability of every trajectory can be analyzed by the energy function approach under certain mild conditions. Real-time digital simulations are performed to validate the effectiveness of the proposed distributed droop control mechanism.

Can Merchant Demand Response Affect Investments in Merchant Energy Storage?

Demand response (DR) and energy storage (ES) technologies are seen as almost perfect substitutes for providing spatiotemporal energy arbitrage in power systems with high penetrations of renewable generation. Using both technologies simultaneously is favorable from the system perspective, but may cause profit scarcity for merchant DR aggregators and ES investors. This paper presents an equilibrium problem with equilibrium constraints (EPEC) that models interactions between the merchant DR aggregator and the merchant ES investor in a competitive electricity market. The proposed EPEC is solved using the diagonalization method on an ISO New England testbed with a prospective renewable generation portfolio and different techno-economic parameters of prospective DR and ES technologies. The case study reveals that the optimal ES investments are sensitive to the availability of DR resources, as well as to the ES capital cost and cycling efficiency.

Dynamic Modeling and Feasibility Analysis of a Solid-State Transformer-Based Power Distribution System

This paper presents a comprehensive state-space dynamic model of a future power distribution system for plug-and-play interface of distributed renewable energy resources and distributed energy storage devices. The system, called the future renewable electric energy delivery and management (FREEDM) system, comprises of multiple solid-state transformers (SSTs), and load, generation, and storage connected to each SST in a distributed network. The system allows for high penetration of renewable generation with energy storage at the distribution level. A physics-based 70th-order state-space average model is first developed considering the physical and controller properties of a single-SST FREEDM system along with its distribution components. This fundamental model is then extended to build a multi-SST FREEDM system for feasibility and dynamics behavior analysis of the entire system, which is essential to ensure system power balance. The full average model with multiple SSTs has been incorporated in an IEEE 34 bus distribution testbed for a scaled analysis of the system.

Probabilistic Framework for Transient Stability Assessment of Power Systems With High Penetration of Renewable Generation

This paper introduces a probabilistic framework for transient stability assessment (TSA) of power systems with high penetration of renewable generation. The critical generators and areas of the system are identified using a method based on hierarchical clustering. Furthermore, statistical analysis of several transient stability indices is performed to assess their suitability for TSA of reduced inertia systems. The proposed framework facilitates robust assessment of transient stability of uncertain power systems with reduced inertia.

Full Stochastic Scheduling for Low-Carbon Electricity Systems

High penetration of renewable generation will increase the requirement for both operating reserve (OR) and frequency response (FR), due to its variability, uncertainty, and limited inertia capability. Although the importance of optimal scheduling of OR has been widely studied, the scheduling of FR has not yet been fully investigated. In this context, this paper proposes a computationally efficient mixed integer linear programming formulation for a full stochastic scheduling model that simultaneously optimizes energy production, OR, FR, and underfrequency load shedding. By using value of lost load (VOLL) as the single security measure, the model optimally balances the cost associated with the provision of various ancillary services against the benefit of reduced cost of load curtailment. The proposed model is applied in a 2030-GB system to demonstrate its effectiveness. The impact of installed capacity of wind generation and setting of VOLL is also analyzed.

Leveraging stochastic differential equations for probabilistic forecasting of wind power using a dynamic power curve

Short-term (hours to days) probabilistic forecasts of wind power generation provide useful information about the associated uncertainty of these forecasts. Standard probabilistic forecasts are usually issued on a per-horizon-basis, meaning that they lack information about the development of the uncertainty over time or the inter-temporal correlation of forecast errors for different horizons. This information is very important for forecast end-users optimizing time-dependent variables or dealing with multi-period decision-making problems, such as the management and operation of power systems with a high penetration of renewable generation. This paper provides input to these problems by proposing a model based on stochastic differential equations that allows generating predictive densities as well as scenarios for wind power. We build upon a probabilistic model for wind speed and introduce a dynamic power curve. The model thus decomposes the dynamics of wind power prediction errors into wind speed forecast errors and errors related to the conversion from wind speed to wind power. We test the proposed model on an out-of-sample period of 1year for a wind farm with a rated capacity of 21MW. The model outperforms simple as well as advanced benchmarks on horizons ranging from 1 to 24h. Copyright © 2016 John Wiley & Sons, Ltd.

Multiobjective Optimization Dispatch for Microgrids With a High Penetration of Renewable Generation

Many benefits can be achieved through the implementation of a Microgrid controller, such as minimized cost, reduction in peak power, power smoothing, greenhouse gas emission reduction, and increased reliability of service. However, most Microgrid controllers found in the literature and in the industry optimize a single objective, which either exacerbates or does not solve the problems with integrating a high penetration of renewable energy. This paper presents a methodology of formulating a multiobjective optimization (MOO) so that each objective is quantified through valuation functions that can be specific to every Microgrid. The proposed approach attains a Pareto-optimal solution by directly comparing the quantified valuation functions and solving as if it were a single-objective optimization (SOO) problem. Three cases of controllers are presented and compared: 1)a base case system with no controller; 2)an SOO that optimizes the cost of energy; and 3)an MOO that optimizes five identified benefits. Results show that the proposed controller can mitigate the negative impacts of volatile generation to levels below that of the system load.

Unbiased economic dispatch in control areas with conventional and renewable generation sources

This paper describes an economic dispatch formulation for a power system control area with high penetration of renewable generation and energy storage. We develop a comprehensive objective function that is unbiased to both the conventional and renewable generation sources. Operating constraints of conventional sources are considered, wind turbine generation is treated as a dispatchable source, and solar photovoltaic generation as negative load. A simulated annealing and particle swarm optimization algorithm are separately applied to minimize the objective function. The results obtained by the proposed economic dispatch formulation show that even a variable energy resource like wind can be dispatched in a manner that benefits the control area as a whole.

Small-Disturbance Angle Stability Control With High Penetration of Renewable Generations

Growth and penetration of renewable energy has been remarkable during the last few years in many power systems around the world. Essentially, there are two major technologies responsible for the growth, namely wind and photovoltaic. The technologies involved in harnessing energy from wind and sun are distinctly different in dynamic characteristics and limitations. Consequently, their influence on stability of power system should not be overlooked. This paper examines the small-disturbance angle stability with high penetration of renewable energy and proposes a methodology to control. The hierarchical principal component analysis, which is a clustering method corresponding to the eigenvalue sensitivity of reactive power control, is utilized to select the renewable generator clusters for different reactive power control scheme. Then, the framework based on structured singular value has been employed, in which the reactive power controls of renewable generator clusters are selected such that the desired robust stability criterion is satisfied. Results obtained in 16-machine 68-bus test system (typically used for small-signal angle stability studies) show the effectiveness of the proposed methodology.

An Interactive Multi-agent Based Real Time Control Framework for the Interconnected Power Grid

The high penetration of renewable generation raises great concerns in grid operation on whether system will have sufficient flexibility for balancing the intermittent variation of renewable generation. With the development of smart grid, technologies like demand response, distribution generation, electric vehicle and energy storage become more popular. It is foreseen that with the increasing participation of these flexible load resources and high penetration of renewable generation, the system operations of the future interconnected power grid will become more complicated.This paper discusses the system operational challenges, analyzes the potentials of load flexibility, then proposes a multi-agent system control framework to support the interactive operations of “Source-Grid-Load”. An interactive leading control process is introduced to lead the flexible resources ramping with a sequence of advisory dispatches, reduce the balancing needs from the frequency control generators.

Supporting high penetrations of renewable generation via implementation of real-time electricity pricing and demand response

The rollout of smart meters raises the prospect that domestic customer electrical demand can be responsive to changes in supply capacity. Such responsive demand will become increasingly relevant in electrical power systems, as the proportion of weather-dependent renewable generation increases, due to the difficulty and expense of storing electrical energy. One method of providing response is to allow direct control of customer devices by network operators, as in the UK ‘Economy 7’ and ‘White Meter’ schemes used to control domestic electrical heating. However, such direct control is much less acceptable for loads such as washing machines, lighting and televisions. This study instead examines the use of real-time pricing of electricity in the domestic sector. This allows customers to be flexible but, importantly, to retain overall control. A simulation methodology for highlighting the potential effects of, and possible problems with, a national implementation of real-time pricing in the UK domestic electricity market is presented. This is done by disaggregating domestic load profiles and then simulating price-based elastic and load-shifting responses. Analysis of a future UK scenario with 15 GW wind penetration shows that during low-wind events, UK peak demand could be reduced by 8–11 GW. This could remove the requirement for 8–11 GW of standby generation with a capital cost of £2.6 to £3.6 billion. Recommended further work is the investigation of improved demand-forecasting and the price-setting strategies. This is a fine balance between giving customers access to plentiful, cheap energy when it is available, but increasing prices just enough to reduce demand to meet the supply capacity when this capacity is limited.

Optimal flexible alternative current transmission system device allocation under system fluctuations due to demand and renewable generation

This study proposes two methods for the optimal placement of flexible alternative current transmission system (FACTS) devices considering variations in demand and renewable generation output. The basic optimisation technique utilised is the differential evolution algorithm and the objective is to minimise the cost of generation. The static performance of the FACTS device is considered here. Simulation shows that with renewable generation present in the network, the system state at peak demand is not always the most suitable state to use for the determination of the optimal FACTS allocation. From this, techniques based on the Monte Carlo simulation are proposed to determine the location for which the operation of FACTS device gives highest benefit in terms of saving cost of conventional generation. These techniques collectively are called renewable uncertainty-based optimal FACTS allocation techniques. This study shows the effectiveness of the techniques in the determination of the optimal FACTS placement for networks with a high penetration of renewable generation.