Demand side management studies on distributed energy resources: A survey

The number of distributed environmentally friendly energy sources and generators necessitates new operating methods and a power network board to preserve or even increase the efficiency and quality of the power supply. Similarly, the growth of matriculates promotes the formation of new institutional systems, in which power and power exchanges become increasingly essential. Because of how an inactive entity traditionally organizes distribution systems, the DG’s connection inevitably changes the system’s qualifications to which it is connected. As a consequence of the Distributed Generation, this presumption is currently legal and non-existent. This article glides on demand side management and analysis on distributed energy resources. Investigation of DSM along with zonal wise classification has been carried out in this survey. Its merits and applications are also presented.Universidad Tecnológica de Bolíva

Demand Side Management Studies on Distributed Energy Resources: A Survey

The number of distributed environmentally friendly energy sources and generators necessitates new operating methods and a power network board to preserve or even increase the efficiency and quality of the power supply. Similarly, the growth of matriculates promotes the formation of new institutional systems, in which power and power exchanges become increasingly essential. Because of how an inactive entity traditionally organizes distribution systems, the DG’s connection inevitably changes the system’s qualifications to which it is connected. As a consequence of the Distributed Generation, this presumption is currently legal and non-existent. This article glides on demand side management and analysis on distributed energy resources. Investigation of DSM along with zonal wise classification has been carried out in this survey. Its merits and applications are also presented

Prosumer behaviour in emerging electricity systems

This dissertation investigates the interface between technology and society in the emerging electricity systems and in particular the role of the energy prosumer in the energy transition. It contributes to the understanding of the role of consumers in emerging electricity systems within the current EU energy policy context where consumer active participation is regarded as "a prerequisite for managing the energy transition successfully and in a cost-effective way". Emerging energy systems are characterized by a high level of complexity, especially for what concerns the behaviour of social actors. Social actors interact through physical and social networks by sharing information and learning from one another through social interactions. These interactions determine self-organization and emergent behaviours in energy consumption patterns and practices. I argue that the best suited tool to study emergent behaviours in energy consumption patterns and practices, and to investigate how consumers' preferences and choices lead to macro behaviours is agent based modelling. To build a sound characterization of the energy prosumer, I review the current social psychology and behavioural theories on sustainable consumption and collect evidence from EU energy prosumers surveys, studies and demand side management pilot projects. I employ these findings to inform the development of an agent based model of the electricity prosumer, Subjective Individual Model of Prosumer – SIMP, and its extended version, SIMP-N, that includes the modelling of the social network. I apply SIMP and SIMP-N models to study the emergence in consumer systems and how values and beliefs at consumer level (as defined by social psychology and behavioural theories and informed by empirical evidence) and social dynamics lead to macro behaviours. More specifically, I explore the diffusion of smart grid technologies enabled services among a population of interacting prosumers and evaluate the impact of such diffusion on individual and societal performance indicators under different policy scenarios and contextual factors. The analysis of the simulation results provides interesting insights on how different psychological characteristics, social dynamics and technological elements can strongly influence consumers' choices and overall system performance. I conclude proposing a framework for an integrated approach to modelling emerging energy systems and markets that extend the SIMP model to also include markets, distribution system operator and the electricity network

供給と需要側を考慮した電源システムのモデリングと評価

Modelling and optimization of sustainable power system and energy network are becoming complex engineering. Demand side resources also need to be planned considering characteristics of district energy supply scenario. This research first analyzes the feasibility of VPP based on scenario of Chongming Island. VPP focuses on expansion of renewable energy and upgrade of efficient appliances, results verify the effectiveness of the VPP concept. Then investigates the techno-economic viability of high variable renewable integration. PV-PHS dispatch scenarious are carried out with constraints, PHS effectively recovers the suppression and decreases the PV power levelized cost. Introduction PV-PHS shifts merit order curve to right, decreasing power generating cost. Thirdly, cost and environmental benefits of optimal designed decentralized energy systems were investigated. Scheduled distributed energy resources could be optimized to benefit the public grid. Performance of dynamic price is investigated based on the social demonstration project experiment. Finally, the conclusions are provided.北九州市立大

Optimized energy consumption model for smart home using improved differential evolution algorithm

Abstract: This paper proposes an improved enhanced differential evolution algorithm for implementing demand response between aggregator and consumer. The proposed algorithm utilizes a secondary population archive, which contains unfit solutions that are discarded by the primary archive of the earlier proposed enhanced differential evolution algorithm. The secondary archive initializes, mutates and recombines candidates in order to improve their fitness and then passes them back to the primary archive for possible selection. The capability of this proposed algorithm is confirmed by comparing its performance with three other wellperforming evolutionary algorithms: enhanced differential evolution, multiobjective evolutionary algorithm based on dominance and decomposition, and non-dominated sorting genetic algorithm III. This is achieved by testing the algorithms’ ability to optimize a multiobjective optimization problem representing a smart home with demand response aggregator. Shiftable and non-shiftable loads are considered for the smart home which model energy usage profile for a typical household in Johannesburg, South Africa. In this study, renewable sources include battery bank and rooftop photovoltaic panels. Simulation results show that the proposed algorithm is able to optimize energy usage by balancing load scheduling and contribution of renewable sources, while maximizing user comfort and minimizing peak-to-average ratio

Microload Management in Generation Constrained Power Systems

The reasons for power systems' outages can be complicated and difficult to pinpoint, but an obvious shortfall in generation compared to electricity demand has been identified as the major cause of load shedding in generation constrained power systems. A sudden rise in demand for electricity on these networks at any time could result in a total collapse of the entire grid. Therefore, in this thesis, algorithms to efficiently allocate the available generation are investigated to prevent the associated hardships and lose experience by the final consumers and the electric utility suppliers, respectively. Heuristic technique is utilised by developing various dynamic programming-based algorithms to achieve the constraints of uniquely controlling home appliances to reduce the overall demands for electricity by the consumers within the grid in context. These algorithms are focused on the consumers' comfort and the associated benefits to the electricity utility company in the long run. The evaluation of the proposed approach is achieved through microload management by employing three main techniques; General Shedding (GS), Priority Based Shedding (PBS) and Excess Reuse Shedding (ERS). These techniques were evaluated using both Grouped and “UnGrouped” microloads based on how efficient the microload managed the available generation to prevent total blackouts. A progressive reduction in excess microload shedding experienced by GS, PBS, and the ERS shows the proposed algorithms' effectiveness. Further, predictive algorithms are investigated for microload forecasting towards microload management to prepare both consumers and the electric utility companies for any impending load shedding. Measuring the forecasting accuracy and the root mean square errors of the models evaluated proved the potential for microload demand prediction

Modelling and analysis of demand response implementation in the residential sector

Demand Response (DR) eliminates the need for expensive capital expenditure on the electricity distribution, transmission and the generation systems by encouraging consumers to alter their power usage through electricity pricing or incentive programs. However, modelling of DR programs for residential consumers is complicated due to the uncertain consumption behavious of consumers and the complexity of schedulling a large number of household appliances. This thesis has investigated the design and the implementation challenges of the two most commonly used DR components in the residential sector, i.e., time of use (TOU) and direct load control (DLC) programs for improving their effectiveness and implementation with innovative strategies to facilitate their acceptance by both consumers and utilities. In price-based DR programs, the TOU pricing scheme is one of the most attractive and simplest approaches for reducing peak electricity demand in the residential sector. This scheme has been adopted in many developed countries because it requires less communication infrastructure for its implementation. However, the implementation of TOU pricing in low and lower-middle income economies is less appealing, mainly due to a large number of low-income consumers, as traditional TOU pricing schemes may increase the cost of electricity for low income residential consumers and adversely affect their comfort levels. The research in this thesis proposes an alternative TOU pricing strategy for the residential sector in developing countries in order to manage peak demand problems while ensuring a low impact on consumers’ monthly energy bills and comfort levels. In this study, Bangladesh is used as an example of a lower-to-middle income developing country. The DLC program is becoming an increasingly attractive solution for utilities in developed countries due to advances in the construction of communication infrastructures as part of the smart grid concept deployment. One of the main challenges of the DLC program implementation is ensuring optimal control over a large number of different household appliances for managing both short and long intervals of voltage variation problems in distribution networks at both medium voltage (MV) and low voltage (LV) networks, while simultaneously enabling consumers to maintain their comfort levels. Another important challenge for DLC implementation is achieving a fair distribution of incentives among a large number of participating consumers. This thesis addresses these challenges by proposing a multi-layer load control algorithm which groups the household appliances based on the intervals of the voltage problems and coordinates with the reactive power from distributed generators (DGs) for the effective voltage management in MV networks. The proposed load controller takes into consideration the consumption preference of individual appliance, ensuring that the consumer’s comfort level is satisfied as well as fairly incentivising consumers based on their contributions in network voltage and power loss improvement. Another significant challenge with the existing DLC strategy as it applies to managing voltage in LV networks is that it does not take into account the network’s unbalance constraints in the load control algorithm. In LV distribution networks, voltage unbalance is prevalent and is one of the main power quality problems of concern. Unequal DR activation among the phases may cause excessive voltage unbalance in the network. In this thesis, a new load control algorithm is developed with the coordination of secondary on-load tap changer (OLTC) transformer for effective management of both voltage magnitude and unbalance in the LV networks. The proposed load control algorithm minimises the disturbance to consumers’ comfort levels by prioritising their consumption preferences. It motivates consumers to participate in DR program by providing flexibility to bid their participation prices dynamically in each DR event. The proposed DR programs are applicable for both developed and developing countries based on their available communication infrastructure for DR implementation. The main benefits of the proposed DR programs can be shared between consumers and their utilities. Consumers have flexibility in being able to prioritise their comfort levels and bid for their participation prices or receive fair incentives, while utilities effectively manage their network peak demand and power quality problems with minimum compensation costs. As a whole, consumers get the opportunity to minimise their electricity bills while utilities are able to defer or avoid the high cost of their investment in network reinforcements

Control in distribution networks with demand side management

The way in which electricity networks operate is going through a period of significant change. Renewable generation technologies are having a growing presence and increasing penetrations of generation that are being connected at distribution level. Unfortunately, a renewable energy source is most of the time intermittent and needs to be forecasted. Current trends in Smart grids foresee the accommodation of a variety of distributed generation sources including intermittent renewable sources. It is also expected that smart grids will include demand management resources, widespread communications and control technologies required to use demand response are needed to help the maintenance in supply-demand balance in electricity systems. Consequently, smart household appliances with controllable loads will be likely a common presence in our homes. Thus, new control techniques are requested to manage the loads and achieve all the potential energy present in intermittent energy sources. This thesis is focused on the development of a demand side management control method in a distributed network, aiming the creation of greater flexibility in demand and better ease the integration of renewable technologies. In particular, this work presents a novel multi-agent model-based predictive control method to manage distributed energy systems from the demand side, in presence of limited energy sources with fluctuating output and with energy storage in house-hold or car batteries. Specifically, here is presented a solution for thermal comfort which manages a limited shared energy resource via a demand side management perspective, using an integrated approach which also involves a power price auction and an appliance loads allocation scheme. The control is applied individually to a set of Thermal Control Areas, demand units, where the objective is to minimize the energy usage and not exceed the limited and shared energy resource, while simultaneously indoor temperatures are maintained within a comfort frame. Thermal Control Areas are overall thermodynamically connected in the distributed environment and also coupled by energy related constraints. The energy split is performed based on a fixed sequential order established from a previous completed auction wherein the bids are made by each Thermal Control Area, acting as demand side management agents, based on the daily energy price. The developed solutions are explained with algorithms and are applied to different scenarios, being the results explanatory of the benefits of the proposed approaches

APPLICATION OF LOAD SHIFTING FOR COMMERCIAL HVAC-R SYSTEMS VIA STATIC AND DYNAMIC EVENT AUTOMATED DEMAND RESPONSE

Life in the modern era is inextricably tied to energy and the unending, ever-growing need for more. The technologies that drive society, in fields such as communication, infrastructure, and heavy industry, are dependent on that electrical energy being reliable and readily available. This places an extreme importance on the power generation sector as a function of meeting the demand required for stability. However, as seen with increased climate volatility due to misused or otherwise mismanaged resources in the heavy industry, and the uncertain and variable renewable energy generation; there must also be ecological as well as economic consideration when discussing modern energy. Therefore, a balance between sustainability and energy demand must be met, to increase the desire for better use of energy resources. This thesis focuses on an application of a demand response (DR) program that may increase the performance of energy resources while also maximizing monetary savings for end-users in the commercial sector. This DR may provide support for the uncertain and intermittent nature of renewable generation. By targeting Heating, Ventilation, Air Conditioning, and Refrigeration loads (HVAC-R) utilities have access to non-critical loads that they may be able to shed during times of high energy demand. This allows for the allocation of less resources during peak hours, which may lead to less strain the electrical grid and could increase the threshold for which additional energy resources will need to be constructed. Based on relay to substitute the analog parts of a refrigerator and a development PC to drive the control logic a closed loop, Automated Demand Response (ADR) is implemented to utilize both static and dynamic Time of Use (TOU) events as well as Critical Peak Pricing (CPP) events. This enables the end user to respond to two types of unique events, resulting in potential increased energy efficiency, and monetary savings for the end user via load shifting