Energy Demand Response

Purpose – In the vast majority of published papers, the optimal reactive power dispatch (ORPD) problem is dealt as a single-objective optimization; however, optimization with a single objective is insuf fi cient to achieve better  operation  performance  of  power  systems.  Multi-objective  ORPD  (MOORPD)  aims  to  minimize simultaneously either the active power losses and voltage stability index, or the active power losses and the voltage deviation. The purpose of this paper is to propose multi-objective ant lion optimization (MOALO) algorithm to solve multi-objective ORPD problem considering large-scale power system in an effort to achieve a good performance with stable and secure operation of electric power systems.

Design/methodology/approach – A MOALO algorithm is presented and applied to solve the MOORPD problem. Fuzzy set theory was implemented to identify the best compromise solution from the set of the non-dominated solutions. A comparison with enhanced version of multi-objective particle swarm optimization (MOEPSO) algorithm and original (MOPSO) algorithm con fi rms the solutions. An in-depth analysis on the findings was conducted and the feasibility of solutions were fully veri fi ed and discussed.

Findings – Three test systems – the IEEE 30-bus, IEEE 57-bus and large-scale IEEE 300-bus – were used to examine the ef fi ciency of the proposed algorithm. The fi ndings obtained amply con fi rmed the superiority of the proposed approach over the multi-objective enhanced PSO and basic version of MOPSO. In addition to that, the algorithm is bene fi tted from good distributions of the non-dominated solutions and also guarantees the feasibility of solutions.

Originality/value – The proposed algorithm is applied to solve three versions of ORPD problem, active power losses, voltage deviation and voltage stability index, considering large -scale power system IEEE 300 bus.

This working paper analyses governance for demand management innovations in Germany, related energy system outcomes, and issues still outstanding. Demand management is understood in its broad sense here to include demand reduction, demand side response, and distributed energy, but it is also understood to be pivotal to an affordable and sustainable energy system transformation. The working paper is informed by the IGov theory of governing for sustainable energy innovations in that governance is also understood in a broad sense to include objectives, policies, regulations and market rules. The paper takes sustainable energy governance to be an iterative process that is contingent upon a variety of domestic political and energy structures that affect choices made, as well as the effectiveness in practice of attempts to govern for sustainable demand innovations. The paper is divided into 5 sections including an introduction and conclusion. The first two sections set the scene by setting out the energy market and political contexts within which governing for demand management takes place. Section 4 is divided into three subsections each of which focuses in more detail on each of the three aspects of demand management outlined above. This includes analysis of the policies focused on enabling demand management, market innovations, issues still outstanding and current attempts to address these issues including those suggested within the government's 2015 White Paper on electricity market reform. The conclusion includes some lesson drawing for British sustainable energy governance. German energy governance has so far supported distributed and community owned renewable generation, allowed sufficient support and space for the entrance of new business models and technologies, as well as enabled new markets in demand reduction and energy efficiency. The paper shows, however, that much needs still to be done to refocus governance away from supply to demand side policies and, in particular, to enable and integrate more demand side response into electricity and heat markets. There is some awareness within energy policymaking communities that demand response and flexibility are cost effective and efficient methods of better integrating markets, but it remains to be seen if current policy suggestions will be sufficient to enable significant improvement.

Following restructuring of power industry, electricity supply to end-use customers has undergone fundamental changes. In the restructured power system, some of the responsibilities of the vertically integrated distribution companies have been assigned to network managers and retailers. Under the new situation, retailers are in charge of providing electrical energy to electricity consumers who have already signed contract with them. Retailers usually provide the required energy at a variable price, from wholesale electricity markets, forward contracts with energy producers, or distributed energy generators, and sell it at a fixed retail price to its clients. Different strategies are implemented by retailers to reduce the potential financial losses and risks associated with the uncertain nature of wholesale spot electricity market prices and electrical load of the consumers. In this paper, the strategic behavior of retailers in implementing forward contracts, distributed energy sources, and demand-response programs with the aim of increasing their profit and reducing their risk, while keeping their retail prices as low as possible, is investigated. For this purpose, risk management problem of the retailer companies collaborating with wholesale electricity markets, is modeled through bi-level programming approach and a comprehensive framework for retail electricity pricing, considering customers' constraints, is provided in this paper. In the first level of the proposed bi-level optimization problem, the retailer maximizes its expected profit for a given risk level of profit variability, while in the second level, the customers minimize their consumption costs. The proposed programming problem is modeled as Mixed Integer programming (MIP) problem and can be efficiently solved using available commercial solvers. The simulation results on a test case approve the effectiveness of the proposed demand-response program based on dynamic pricing approach on reducing the retailer's risk and increasing its profit.

In this paper, the production decisions across multiple energy suppliers in smart grid, powering cellular networks are investigated. The suppliers are characterized by different offered prices and pollutant emissions levels. The challenge is to decide the amount of energy provided by each supplier to each of the operators such that their profitability is maximized while respecting the maximum tolerated level of CO2 emissions. The cellular operators are characterized by their offered quality of service (QoS) to the subscribers and the number of users that determines their energy requirements. Stochastic geometry is used to determine the average power needed to achieve the target probability of coverage for each operator. The total average power requirements of all networks are fed to an optimization framework to find the optimal amount of energy to be provided from each supplier to the operators. The generalized alphafair utility function is used to avoid production bias among the suppliers based on profitability of generation. Results illustrate the production behavior of the energy suppliers versus QoS level, cost of energy, capacity of generation, and level of fairness.