Renewable Generation Output Research Articles

A methodology to assess the economic impact of power storage technologies.

We present a methodology for assessing the economic impact of power storage technologies. The methodology is founded on classical approaches to the optimal stopping of stochastic processes but involves an innovation that circumvents the need to, ex ante, identify the form of a driving process and works directly on observed data, avoiding model risks. Power storage is regarded as a complement to the intermittent output of renewable energy generators and is therefore important in contributing to the reduction of carbon-intensive power generation. Our aim is to present a methodology suitable for use by policy makers that is simple to maintain, adaptable to different technologies and easy to interpret. The methodology has benefits over current techniques and is able to value, by identifying a viable optimal operational strategy, a conceived storage facility based on compressed air technology operating in the UK.This article is part of the themed issue 'Energy management: flexibility, risk and optimization'.

Efficient Uncertainty Quantification in Stochastic Economic Dispatch

Stochastic economic dispatch models address uncertainties in forecasts of renewable generation output by considering a finite number of realizations drawn from a stochastic process model, typically via Monte Carlo sampling. Accurate evaluations of expectations or higher order moments for quantities of interest, e.g., generating cost, can require a prohibitively large number of samples. We propose an alternative to Monte Carlo sampling based on polynomial chaos expansions. These representations enable efficient and accurate propagation of uncertainties in model parameters, using sparse quadrature methods. We also use Karhunen–Loeve expansions for efficient representation of uncertain renewable energy generation that follows geographical and temporal correlations derived from historical data at each wind farm. Considering expected production cost, we demonstrate that the proposed approach can yield several orders of magnitude reduction in computational cost for solving stochastic economic dispatch relative to Monte Carlo sampling, for a given target error threshold.

Impact Analysis of Demand Response Intensity and Energy Storage Size on Operation of Networked Microgrids

Integration of demand response (DR) programs and battery energy storage system (BESS) in microgrids are beneficial for both microgrid owners and consumers. The intensity of DR programs and BESS size can alter the operation of microgrids. Meanwhile, the optimal size for BESS units is linked with the uncertainties associated with renewable energy sources and load variations. Similarly, the participation of enrolled customers in DR programs is also uncertain and, among various other factors, uncertainty in market prices is a major cause. Therefore, in this paper, the impact of DR program intensity and BESS size on the operation of networked microgrids is analyzed while considering the prevailing uncertainties. The uncertainties associated with forecast load values, output of renewable generators, and market price are realized via the robust optimization method. Robust optimization has the capability to provide immunity against the worst-case scenario, provided the uncertainties lie within the specified bounds. The worst-case scenario of the prevailing uncertainties is considered for evaluating the feasibility of the proposed method. The two representative categories of DR programs, i.e., price-based and incentive-based DR programs are considered. The impact of change in DR intensity and BESS size on operation cost of the microgrid network, external power trading, internal power transfer, load profile of the network, and state-of-charge (SOC) of battery energy storage system (BESS) units is analyzed. Simulation results are analyzed to determine the integration of favorable DR program and/or BESS units for different microgrid networks with diverse objectives.

Stochastic valuation of energy storage in wholesale power markets

Energy storage systems are well poised to mitigate uncertainties of renewable generation outputs. Grid-scale energy storage projects are major investments which call for rigorous valuation and risk analysis. This paper provides a stochastic energy storage valuation framework in wholesale power markets which considers all key revenue streams simultaneously. As part of this framework, an operational optimization model is developed to determine the energy storage system's optimal dispatch sequences. A future curve model is built to capture the volatilities of electricity prices. In addition, a frequency regulation service price forecasting model is developed. Simulation results with a realistic battery storage system reveal that the majority of the market revenues comes from frequency regulation services. Simulation results also show that both round-trip efficiency and power-to-energy ratio are crucial to the cost effectiveness of energy storage systems.

Strong Formulations for Multistage Stochastic Self-Scheduling Unit Commitment

With the increasing penetration of renewable energy into the power grid system, the volatility of real-time electricity prices increases significantly. This brings challenges for independent power producers to provide optimal bidding strategies. The traditional approaches of only attending the day-ahead market might not be profitable enough without taking advantage of real-time price volatility. In this paper, we study the optimal bidding strategies for the independent power producers utilizing self-scheduling strategies to participate in the real-time market considering real-time electricity price volatility, with the objective of maximizing the total expected profit. Considering the correlations of renewable energy generation outputs among different time periods, the correlations of real-time prices are captured in our modeling framework, in which we explore a multistage stochastic scenario tree to formulate the price uncertainties. Accordingly, the derived multistage stochastic self-scheduling unit commitment problem is transformed as a deterministic equivalent mixed-integer linear programming formulation. To overcome the curse of dimensionality, we develop strong valid inequalities for the derived stochastic unit commitment polytope to speed up the algorithms to solve the problem. In particular, we derive strong valid inequalities that can provide the convex hull descriptions for the two-period case and a special class of the three-period cases with rigorous proofs provided. Furthermore, strong valid inequalities, including facet-defining proofs, for multistage cases are proposed to further strengthen the model. Finally, numerical experiments verify the effectiveness of our derived strong valid inequalities by incorporating them in a branch-and-cut framework.

Impacts of Feeder Reconfiguration on Renewable Resources Allocation in Balanced and Unbalanced Distribution Systems

In this article, network reconfiguration and distributed generation allocation in distribution networks are dealt with simultaneously while imposing an objective of minimizing energy loss. The proposed method, which is based on a genetic algorithm, takes into consideration the uncertainty related to renewable distributed generation output power and the load variability. Three scenarios are assessed to analyze the superiority of the proposed method. In the first scenario, distributed generation units are allocated using the base configuration, followed by network reconfiguration. In the second scenario, distributed generations are allocated after network reconfiguration. In the third scenario, distributed generations are allocated simultaneously with network reconfiguration. The constraints involved include voltage limits, line current limits, and radial topology. Both balanced and unbalanced distribution systems are used as case studies.

Distributionally Robust Co-Optimization of Energy and Reserve Dispatch

This paper proposes a two-stage distributionally robust optimization model for the joint energy and reserve dispatch (D-RERD for short) of bulk power systems with significant renewable energy penetration. Distinguished from the prevalent uncertainty set-based and worst-case scenario oriented robust optimization methodology, we assume that the output of volatile renewable generation follows some ambiguous distribution with known expectations and variances, the probability distribution function (pdf) is restricted in a functional uncertainty set. D-RERD aims at minimizing the total expected production cost in the worst renewable power distribution. In this way, D-RERD inherits the advantages from both stochastic optimization and robust optimization: statistical characteristic is taken into account in a data-driven manner without requiring the exact pdf of uncertain factors. We present a convex optimization-based algorithm to solve the D-RERD, which involves solving semidefinite programming (SDP), convex quadratic programming (CQP), and linear programming (LP). The performance of the proposed approach is compared with the emerging adaptive robust optimization (ARO)-based model on the IEEE 118-bus system. Their respective features are discussed in case studies.

The Stochastic Bidding Strategy of Micro grid for Participation in Energy and Ancillary Services

Typically, micro-grid is considered as an effective way to integration of distributed generations. However, in the deregulated environment integration of micro-grid in power system should be further considered. In this paper, a stochastic bidding strategy of joint market for the energy and spinning reserve services markets, with taking into account of the uncertainties of renewable distributed generation output power, load and electricity market prices to maximize the profits of micro-grid, is recommended. The bidding strategy is modeled as an optimization problem and is divided into two stages. First the predictions for uncertainties was carried out by using Adaptive Neural Fuzzy Inference System(ANFIS) and according to the forecast errors and Latin Hyperbolic Sampling (LHS) method, scenarios for micro-grid produced and these scenarios is reduced by using backward scenario reducing. In the second stage, according to the generated scenarios in the previous stage, the profit of micro-grid bidding including the revenue of micro-grid bidding, operation and imbalance costs is optimized by genetic algorithm. In the end, micro-grid bids is sent to market. The effectiveness of the proposed method is discussed on a sample micro-grid and results show the effectiveness of the proposed method.

Renewable-load matching dispatch for isolated power systems with intermittent renewable sources

This paper proposes a renewable-load matching approach for dispatching generation resources in power systems with high renewable power penetrations. In order to utilise the capability of renewable resources to follow the load variations, the proposed matching approach is to restrict the output of renewable generators by a load-related bound in each dispatch time period. The dispatch problem is formulated to minimise the system cost comprising fuel, emission, and regulation costs as well as renewable power curtailment penalty. Compared with the traditional approach of treating renewable resources as negative loads, the proposed matching approach can significantly reduce the system cost by including the dynamic control of renewable power output. Moreover, the multi-objective function provides a way to study the trade-off among different costs in power systems with renewable resources. Solution is obtained by transforming the optimisation problem into its equivalent form and using local search algorithms. Simulations are implemented on a specific isolated power system with four generators to demonstrate the advantage of proposed approach.

우리나라 비중앙급전발전기의 하루전 출력 예측시스템 개발

This paper proposes a probabi listic generatio n assessment mo del of renewable energy generators(REGs) considering uncertainty of resources, mainly focused on Wind Turbine Genera tor(WTG) and Solar Cell Generator(SCG) which are dispersed widely in South Korea The propos ed numerical analysis method as sesses the one day-ahead generation by combining equivalent generation characteristics function and probabilisti c distribution function of wind speed(WS) and solar radiation(S R) resources. The equivalent generat ion functions(EGFs) of the win d and solar farms are established by grouping a lot of the farms appropriately centered on W eather Measurement Station(WMS ). First, the EGFs are assessed by using regression analysis method based on typical least squ are method from the recorded a ctual generation data and hist orical resources(WS and SR). Second, the generation of the REGs is assessed by adding the on e day-ahead resources forecast, announced by WMS, to the EGFs which are formulated as third order degree polynomials usi ng the regression analysis. Third, a Renewable Energy Generation Assessment System(REGA S) including D/B of recorded a ctual generation data and hist orical resources is developed using the model and algorithm pre dicting one day-ahead power ou tput of renewable energy generators.

Dispatch Strategy of PHEVs to Mitigate Selected Patterns of Seasonally Varying Outputs From Renewable Generation

Rapid development of plug-in hybrid electric vehicles (PHEVs) brings new challenges and opportunities to the power industry. A large number of idle PHEVs can potentially be employed to form a distributed energy storage system for supporting renewable generation. To reduce the negative effects of unsteady renewable generation outputs, a stochastic optimization-based dispatch model capable of handling uncertain outputs of PHEVs and renewable generation is formulated in this paper. The mathematical expectations, second-order original moments, and variances of wind and photovoltaic (PV) generation outputs are derived analytically. Incorporated all the derived uncertainties, a novel generation shifting objective is proposed. The cross-entropy (CE) method is employed to solve this optimal dispatch model. Multiple patterns of renewable generation depending on seasons and renewable market shares are investigated. The feasibility and efficiency of the developed optimal dispatch model, as well as the CE method, are demonstrated with a 33-node distribution system.

Model-Based Estimation of Energy Savings in Load Control Events for Thermostatically Controlled Loads

Load control (LC) of populations of air conditioners (ACs) is considered suitable to shift energy from on- to off-peak times, and track the intermittent power output of renewable generation. From a technical and economical point of view, it is paramount to quantify the amount of energy that can be saved by implementing these LC events. This paper proposes a new causal methodology to estimate such energy savings using a Kalman filter that includes a parametric second-order model of the aggregate demand of a population of ACs. The proposed methodology relies only on readings of aggregate electrical power at the feeder level and does not require historical load data, or a control group, and hence, it can be used where other methods reported in the literature are inapplicable. The proposed estimator is evaluated on a numerical case study that embeds simulated ACs in real power and temperature data from a 70-house residential precinct.

Modeling and analysis of renewable energy obligations and technology bandings in the UK electricity market

In the UK electricity market, generators are obliged to produce part of their electricity with renewable energy resources in accordance with the Renewable Obligation Order. Since 2009 technology banding has been added, meaning that different technologies are rewarded with a different number of certificates. We analyze these two different renewable obligation policies in a mathematical representation of an electricity market with random availabilities of renewable generation outputs and random electricity demand. We also present another, alternative, banding policy. We provide revenue adequate pricing schemes for the three obligation policies. We carry out a simulation study via sampling. A key finding is that the UK banding policy cannot guarantee that the original obligation target is met, hence potentially resulting in more pollution. Our alternative provides a way to make sure that the target is met while supporting less established technologies, but it comes with a significantly higher consumer price. Furthermore, as an undesirable side effect, we observe that a cost reduction in a technology with a high banding (namely offshore wind) leads to more CO2 emissions under the UK banding policy and to higher consumer prices under the alternative banding policy.

A Real-Time Power Allocation Algorithm and its Communication Optimization for Geographically Dispersed Energy Storage Systems

The paper presents a distributed algorithm that regulates the power outputs of multiple dispersed energy storage systems (DESS), which can be used to provide desirable services for power systems, such as renewable generation output smoothing and secondary control. The algorithm is based on the cooperative control principle of network control theory and it satisfies both the power balance requirement of power systems and the fair utilization among the DESS. The most distinct feature of the algorithm is that each DESS only requires the information from its neighbors through some local communication networks (CN), utilizing a virtual leader embedded in one or several DESS to receive a power command signal. In addition, to improve the robustness of the CN among DESS, two optimization models are formulated to design the so-called “ N-1” redundant network, while considering both economic issues and convergence rate. These features enable the DESS under the algorithm to have both self-organizing and adaptive coordination properties under some adverse conditions. Simulation on the IEEE 123-bus distribution system validates the effectiveness of the proposed algorithm.

Assessment of energy supply and continuity of service in distribution network with renewable distributed generation

Continuity of electricity supply with renewable distributed generation (DG) is a topical issue for distribution system planning and operation, especially due to the stochastic nature of power generation and time varying load demand. The conventional adequacy and reliability analysis methods related to bulk generation systems cannot be applied directly for the evaluation of adequacy criteria such as ‘energy supply’ and ‘continuity of service’ for distribution networks embedded with renewable DG. In this paper, new indices highlighting ‘available supply capacity’ and ‘continuity of service’ are proposed for ‘energy supply’ and ‘continuation of service’ evaluation of generation-rich distribution networks, and analytical techniques are developed for their quantification. A probability based analytical method has been developed using the joint probability of the demand and generation, and probability distributions of the proposed indices have been used to evaluate the network adequacy in energy supply and service continuation. A data clustering technique has been used to evaluate the joint probability between coincidental demand and renewable generation. Time sequential Monte Carlo simulation has been used to compare the results obtained using the proposed analytical method. A standard distribution network derived from Roy Billinton test system and a practical radial distribution network have been used to test the proposed method and demonstrate the estimation of the well-being of a system for hosting renewable DG units. It is found that renewable DG systems improve the ‘energy supply’ and ‘continuity of service’ in the distribution networks. The results suggest that the consideration of the time varying demand and stochastic renewable generation output has significant impact on the ‘energy supply’ and ‘continuity of service’ in the distribution networks.

Branch Current Control on a Superconducting DC Grid

The challenges of transferring large levels of renewable generation output from remote locations to load centers through congested ac transmission networks has renewed interest in high-voltage dc (HVDC) transmission. Mesh-connected HVDC grids are under consideration to maximize utilization of available transmission accessible corridors. High-temperature superconducting (HTS) cables are candidates for future implementations of future dc grids with their high current capabilities and low losses. The branch current flows in a conventional HVDC grid are determined by voltage differences between the ends of a line, making it difficult to regulate currents in different paths. This becomes even more difficult in a superconducting grid where the steady-state voltage is equal across the system, and line current flows only change during temporary voltage differences. If a line needs to be removed from service for maintenance it is difficult to bring the current in that line to zero without de-energizing the entire system. In addition, once the line is re-energized it will not carry any load current until a voltage difference occurs. This paper will explore options to control individual branch currents on an HTS mesh by exploring controllable circuit configurations without the need to add series elements. A control scheme will be developed for a dc grid with a controllable configuration.

Expanding interconnection capacity to integrate intermittent generation in the Iberian Peninsula

Increasing penetration of intermittent energy sources in power systems has been requiring higher operational flexibility and cross-border transmission capacity. Intermittent generation, especially wind and solar power, has grown significantly in the Iberian Peninsula during the last decade and it will keep growing in the next year. Owing to limited interconnection capacity with the French and the Moroccan power systems, generation intermittency has been dealt with mostly within Spain and Portugal, helped by a flexible generation mix. Despite the relatively high flexibility of the Iberian system, in some situations, renewable generation output had to be curtailed. In general, renewable energy curtailment is an emergency action taken in case of network constraints or in case of generation surplus at single node. This emergency action is expected to be more common in the near future if no additional operating measures are taken. This study focuses on the expansion of interconnection capacity that would be required in order to integrate intermittent generation surplus in the Iberian Peninsula in the period 2020-2050. For this purpose, the trade-off between transmission investment costs and the costs of renewable energy curtailment is analysed. Results for different intermittent generation levels and price scenarios are presented and discussed.

Modeling and Analysis of Renewable Energy Obligations and Technology Bandings in the UK Electricity Market

In the UK electricity market generators are obliged to produce a certain amount of their electricity with renewable energy resources in accordance with the Renewable Obligation Order. This obligation comes with an (indirect) subsidy rewarding firms with tradable green certificates for their renewable energy production. Until 2009 every unit production of renewable energy was rewarded with the same amount of certificates. Since 2009 a banding has been added to the Renewable Order, meaning that different technologies are rewarded with a different number of certificates. The obligation then shifted from one on production to one on certificates. This paper discusses and analyzes these two different renewable obligation policies in a mathematical framework. The policies are modeled into a two-stage electricity market investment model in which firms invest at the first stage and produce at the second stage. Since banding may result in an outcome where the original obligation target is not satisfied, hence potentially resulting in more pollution, we present an alternative banding policy. We provide revenue adequate pricing schemes for the three obligation policies in order to guarantee that the regulator can cover the payments to firms owning certificates with the mark-ups paid by consumers. Then we move to a stochastic framework where the two main sources of uncertainty are the availability of renewable generation output and the stochastic nature of electricity demand. The stochastic framework is used to carry out a simulation study via sampling on a small network representing the UK market. We analyze the effects of the three obligation policies; a key finding is that, indeed, the UK banding policy cannot guarantee that the original obligation target is met. Our alternative provides a way to make sure that the target is met while supporting less established technologies, but it comes with a significantly higher consumer price. Furthermore, we observe a concerning side effect of banding policies, as a cost reduction in a technology with a high banding (namely offshore wind) leads to more CO2 emissions under the UK banding policy and to higher consumer prices under the alternative banding policy.

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.