Given the increasing penetration in renewable generation, the UK power system is experiencing a decline in system inertia and an increase in frequency response (FR) requirements. Faster FR products are a mitigating solution that can cost-effectively meet the system balancing requirements. Thus, this paper proposes a mixed integer linear programming (MILP) unit commitment model which can simultaneously schedule inertial response, mandatory FR, as well as a sub-second FR product - enhanced frequency response (EFR). The model quantifies the value of providing faster reacting FR products in comparison with other response times from typical FR products. The performance and value of EFR are determined in a series of future energy scenarios with respect to the UK market and system conditions.
Previous works estimate FR requirements by adopting different methodologies, from pre-defined, generation-based requirements to inertia-dependent ones. In order to assess the economic benefits of dynamic demand in providing frequency regulation, @cite_6 constructed a unit commitment (UC) model with per-defined frequency regulation and reserve requirements. Similarly, @cite_15 developed a comprehensive UC model for the UK power system and used historical reserve requirements to estimate future electricity and reserve prices. System inertia can capture transient frequency evolution dynamics to accurately determine hourly-based FR requirements. Chavez2014a uses the system inertia to estimate PFR adequacy. Recent works @cite_5 successfully conducted the stochastic scheduling for generation dispatch and ancillary services with inertia-dependent constraints. Regarding the multi-speed FRs, @cite_12 formulates the model that can simultaneously allocate inertia responses (IR), PR and EFR, and evaluates the EFR effectiveness. Considering different inertia conditions, @cite_7 proposed a utility function for system operator (SO) to choose multi-speed FR products.
@cite_7 @cite_6 @cite_5 @cite_15 @cite_12
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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. We consider the problem of resource allocation in downlink OFDMA systems for multi service and unknown environment. Due to users' mobility and intercell interference, the base station cannot predict neither the Signal to Noise Ratio (SNR) of each user in future time slots nor their probability distribution functions. In addition, the traffic is bursty in general with unknown arrival. The probability distribution functions of the SNR, channel state and traffic arrival density are then unknown. Achieving a multi service Quality of Service (QoS) while optimizing the performance of the system (e.g. total throughput) is a hard and interesting task since it depends on the unknown future traffic and SNR values. In this paper we solve this problem by modeling the multiuser queuing system as a discrete time linear dynamic system. We develop a robust H∞ controller to regulate the queues of different users. The queues and Packet Drop Rates (PDR) are controlled by proposing a minimum data rate according to the demanded service type of each user. The data rate vector proposed by the controller is then fed as a constraint to an instantaneous resource allocation framework. This instantaneous problem is formulated as a convex optimization problem for instantaneous subcarrier and power allocation decisions. Simulation results show small delays and better fairness among users. Increase of penetration of intermittent renewable power connected to the system will increase the requirements for frequency regulation services. If these services are met by conventional plant running part-loaded, this will not only reduce the system operational efficiency but will also limit the ability of the system to accommodate renewable generation. This work quantifies the value of Dynamic Demand (DD) concept, which enables domestic refrigeration appliances to contribute to primary frequency regulation through an advanced stochastic control algorithm. The benefits of DD providing frequency response are determined for a wide range of future low-carbon generation systems, using an efficient generation scheduling model which includes scheduling of frequency regulation and reserve services. The analysis also considers the potential impact of wind generation on system inertia and primary frequency regulation. Simulations indicate that the benefits of DD increase considerably in systems with high wind penetration, making DD an attractive option for significantly improving system efficiency. Considerable developments in the real-time telemetry of demand-side systems allow independent system operators (ISOs) to use reserves provided by demand response (DR) in ancillary service markets. Currently, many ISOs have designed programs to utilize the reserve provided by DR in electricity markets. This paper presents a stochastic model to schedule reserves provided by DR in the wholesale electricity markets. Demand-side reserve is supplied by demand response providers (DRPs), which have the responsibility of aggregating and managing customer responses. A mixed-integer representation of reserve provided by DRPs and its associated cost function are used in the proposed stochastic model. The proposed stochastic model is formulated as a two-stage stochastic mixed-integer programming (SMIP) problem. The first-stage involves network-constrained unit commitment in the base case and the second-stage investigates security assurance in system scenarios. The proposed model would schedule reserves provided by DRPs and determine commitment states of generating units and their scheduled energy and spinning reserves in the scheduling horizon. The proposed approach is applied to two test systems to illustrate the benefits of implementing demand-side reserve in electricity markets.