https://paginas.fe.up.pt/~cdm/DE2/DG_definition.pdf

Distributed generation : a definition

The paper reviews different approaches, technologies, and strategies to manage large-scale schemes of variable renewable electricity such as solar and wind power. We consider both supply and demand side measures. In addition to presenting energy system flexibility measures, their importance to renewable electricity is discussed. The flexibility measures available range from traditional ones such as grid extension or pumped hydro storage to more advanced strategies such as demand side management and demand side linked approaches, e.g. the use of electric vehicles for storing excess electricity, but also providing grid support services. Advanced batteries may offer new solutions in the future, though the high costs associated with batteries may restrict their use to smaller scale applications. Different “P2Y”-type of strategies, where P stands for surplus renewable power and Y for the energy form or energy service to which this excess in converted to, e.g. thermal energy, hydrogen, gas or mobility are receiving much attention as potential flexibility solutions, making use of the energy system as a whole. To “functionalize” or to assess the value of the various energy system flexibility measures, these need often be put into an electricity/energy market or utility service context. Summarizing, the outlook for managing large amounts of RE power in terms of options available seems to be promising.

https://research.aalto.fi/files/6557604/lund_et_al_review.pdf

Review of energy system flexibility measures to enable high levels of variable renewable electricity

This paper discusses conceptual frameworks for actively involving highly distributed loads in power system control actions. The context for load control is established by providing an overview of system control objectives, including economic dispatch, automatic generation control, and spinning reserve. The paper then reviews existing initiatives that seek to develop load control programs for the provision of power system services. We then discuss some of the challenges to achieving a load control scheme that balances device-level objectives with power system-level objectives. One of the central premises of the paper is that, in order to achieve full responsiveness, direct load control (as opposed to price response) is required to enable fast time scale, predictable control opportunities, especially for the provision of ancillary services such as regulation and contingency reserves. Centralized, hierarchical, and distributed control architectures are discussed along with benefits and disadvantages, especially in relation to integration with the legacy power system control architecture. Implications for the supporting communications infrastructure are also considered. Fully responsive load control is illustrated in the context of thermostatically controlled loads and plug-in electric vehicles.

/pdf/achieving-controllability-of-electric-loads-2zgxun0ikw.pdf

Achieving Controllability of Electric Loads

Demand side management (DSM) is one of the important functions in a smart grid that allows customers to make informed decisions regarding their energy consumption, and helps the energy providers reduce the peak load demand and reshape the load profile. This results in increased sustainability of the smart grid, as well as reduced overall operational cost and carbon emission levels. Most of the existing demand side management strategies used in traditional energy management systems employ system specific techniques and algorithms. In addition, the existing strategies handle only a limited number of controllable loads of limited types. This paper presents a demand side management strategy based on load shifting technique for demand side management of future smart grids with a large number of devices of several types. The day-ahead load shifting technique proposed in this paper is mathematically formulated as a minimization problem. A heuristic-based Evolutionary Algorithm (EA) that easily adapts heuristics in the problem was developed for solving this minimization problem. Simulations were carried out on a smart grid which contains a variety of loads in three service areas, one with residential customers, another with commercial customers, and the third one with industrial customers. The simulation results show that the proposed demand side management strategy achieves substantial savings, while reducing the peak load demand of the smart grid.

Demand Side Management in Smart Grid Using Heuristic Optimization

This paper presents a novel appliance commitment algorithm that schedules thermostatically controlled household loads based on price and consumption forecasts considering users' comfort settings to meet an optimization objective such as minimum payment or maximum comfort. The formulation of an appliance commitment problem is described using an electrical water heater load as an example. The thermal dynamics of heating and coasting of the water heater load is modeled by physical models; random hot water consumption is modeled with statistical methods. The models are used to predict the appliance operation over the scheduling time horizon. User comfort is transformed to a set of linear constraints. Then, a novel linear-sequential-optimization-enhanced, multiloop algorithm is used to solve the appliance commitment problem. The simulation results demonstrate that the algorithm is fast, robust, and flexible. The algorithm can be used in home/building energy-management systems to help household owners or building managers to automatically create optimal load operation schedules based on different cost and comfort settings and compare cost/benefits among schedules.

Appliance Commitment for Household Load Scheduling

Distribution Automation and Control (DAC) systems have potentially major effects on costs, social impacts, and even on the nature of the power system itself, especially as dispersed storage, generation, and customer interaction become more prevalent. However, at the present time, it is not clear which particular modes of control will best exploit the capabilities of DAC. Homeostatic Utility Control is an overall concept which tries to maintain an internal equilibrium between supply and demand. Equilibrating forces are obtained over longer time scales (5 minutes and up) by economic principles through an Energy Marketplace using time-varying spot prices. Faster supply-demand balancing is obtained by employing "governor-type" action on certain types of loads using a Frequency Adaptive Power Energy Rescheduler (FAPER) to assist or even replace conventional turbine-governed systems and spinning reserve. Conventional metering is replaced by a Marketing Interface to Customer (MIC) which, in addition to measuring power usage, multiplies that usage by posted price and records total cost. Customers retain the freedom to select their consumption patterns. Homeostatic control is a new, untried concept. It is discussed in this paper because its great potential makes it a vehicle for interesting discussions of where the future may actually evolve.

Homeostatic Utility Control

The customer response to spot prices is discussed. The factors that allow flexible customer response without service curtailments are identified. An overview of a fast, optimal, nonsimplex algorithm applicable to single storage electricity consuming processes is presented. A case study involving an air compression company demonstrates the application of the algorithm and shows the economic effects of industrial customer response to the spot pricing of electricity. >

Optimal demand-side response to electricity spot prices for storage-type customers

The transmission system, once the necessary but nearly invisible component of the electric power system is now the focus of physical/electrical and financial attention of the utility and, more significantly their potential suppliers/customers This paper has three primary objectives The first is to present a discussion of a new paradigm for the electric power industry The second is to review transmission pricing options, specifically those utilizing either long run (LRMC) or short run (SRMC) marginal cost based approaches The third is to provide a review of innovative approaches to transmission asset management and finance as seen from international experience >

Transmission system management and pricing: new paradigms and international comparisons

A description is given of the algorithms designed for use in residential load control systems. The authors present the functions to be fulfilled by such a price-responsive device and describe the end-user devices available in residences and the control logics applicable to each. It is concluded that there is a need to understand customer attitudes and acceptance in the design of the response strategies and in the design of the man-machine interface. >

Algorithms for a spot price responding residential load controller

Non-dispatchable technologies (solar, wind, run-of-the-ri ver hydro, cogeneration) affect the cost of electricity production in a complex manner by modifying the probability distribution of demand for conventional generation The lack of an appropriate methodology to efficiently derive this modification has prevented inclusion of non-dispatchable technologies as decision variables in capacity expansion models This paper develops a stochastic approach to load modification which explicitly models two types of interdependencies between load and non-dispatchable generation: through time of day and through weather If more than one non-dispatchable generation technology is considered, the dependency among them is also modeled Furthermore, the total as well as the marginal impact of non-dispatchable capacity on system reliability and operating cost is derived This allows us to model non-dispatchable generation in the context of two broad classes of optimization algorithms Dynamic programming and mathematical decomposition are considered in this paper as characteristic examples of algorithms in each class Load modification models already in use derive total cost impact only, and are based on hourly chronological simulation which is a computationally cumbersome method The methodology developed here provides marginal impact in addition to total impact values, is computationally efficient and is applicable to future demand projections at almost any level of detail Finally, its accuracy proved very satisfactory when tested on 1975 Miami load and insolation data

Richard D. Tabors

Papers

Homeostatic Utility Control

Optimal demand-side response to electricity spot prices for storage-type customers

Transmission system management and pricing: new paradigms and international comparisons

Algorithms for a spot price responding residential load controller

The Introduction of Non-Dispatchable Technologies as Decision Variables in Long-Term Generation Expansion Models