- Email: [email protected]
Electricity agents in smart grid markets
G Model
COMIND-2397; No. of Pages 7 Computers in Industry xxx (2012) xxx–xxx
Contents lists available at SciVerse ScienceDirect
Computers in Industry journal homepage: www.elsevier.com/locate/compind
Electricity agents in smart grid markets Salem Al-Agtash Department of Computer Engineering, German-Jordanian University, P.O. Box 35247, Amman 11180, Jordan
A R T I C L E I N F O
A B S T R A C T
Article history: Received 21 March 2012 Received in revised form 11 October 2012 Accepted 18 October 2012 Available online xxx
This paper presents a novel architecture of electricity agents in smart grid markets. The architecture implements a middleware that allows standard agent plug-ins representing ‘‘smart grid’’ elements in a two-way power flow. Agent operations are automated to allow demand variations and exploring a wider spectrum of trade opportunities in an efficient manner, while coordinating with the system operator for reliability, security, and stability. We give trade and operation computational models of power grid components and their interaction protocols and authentication in a multi-agent framework. Trade models are both pool and multilateral based. Operation models respect power dispatch limits, generation and transmission constraints, and spinning reserve requirements. The models are coded as part of the agent software. The protocols are KQML (Knowledge Query Manipulation Language) based communication. An IEEE 5-bus grid system is used for illustration. The testing results for different load profiles show better distribution of market sales and a market clearing price convergence very close to the marginal cost of generation supply, thus an implication of economic efficiency of energy resources, as well as security and power quality compared to traditional electricity trade models based on Cournot and Bertrand estimated clearing prices. ß 2012 Elsevier B.V. All rights reserved.
Keywords: Electricity markets Smart grid middleware Agent based modeling
1. Introduction Smart grid computing in power industry has become a trend, worldwide. The objective is to use smart technologies in today’s aging power grid for operation, control, and management, while unbundling generation, transmission, and distribution. This trend motivated new business models of electricity systems. Diverse electricity elements need to operate and interact in heterogeneous and yet decentralized decision structures. As power markets continue to evolve in a smart grid context, there is a growing consensus toward developing a sophisticated computing infrastructure. The infrastructure will have to accommodate complexity requirements of operation, interaction, and coordination of an integrated system with two-way electric power flow. The flow of power is not only from the grid to customers, but also from customers to the grid when customers have surplus of solar, wind, or any other renewable energy sources. The smart grid is widely recognized as the next generation electricity network [6–8]. It is based on communication and information infrastructure that can intelligently integrate and coordinate generation, distribution, and transmission. The smart grid is visualized to consist of three main components [6]: (1) demand management to monitor and manage consumption through smart meters, appliances, and programs; (2) distributed
E-mail address: [email protected].
electricity generation to accelerate widespread installation of renewable power sources; and (3) transmission and distribution grid management to monitor and control power grid in real time. In Ref. [7], it was argued that new policy interventions are needed for a systemic perspective to consolidate technical and economic synergies that allow individual smart grid technologies reverse the consumption-driven paradigm of the electricity sector. Opportunities, challenges, and uncertainties associated with the ‘‘smart grid’’ paradigm have been discussed in [8], with a focus on deployment issues of smart grid technologies at the high voltage transmission, local distribution, and end-use metering. The current technical research has focused in a great deal on developing supply bidding strategies, demand side management, locational marginal pricing, system congestion, and many other issues and scenarios of electricity markets. The ‘‘smart grid electricity system’’ adds more complexity to understanding, analyzing, and modeling of electric power industry. Complexity is in the communication and coordination between the diverse smart grid components that need to inter-operate and create a secure, economic, reliable, and yet an efficient smart electric power system [5,19,20]. In Ref. [5], the authors present the complexity of communication and coordination of huge data points representing every single control action and variable in every node in the whole integrated power system. The flow of data goes to/from the control center, the substations, automatic generation controls, and distribution bus systems. A real-time information architecture supported by high-speed networks has been introduced on the
0166-3615/$ – see front matter ß 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.compind.2012.10.009
Please cite this article in press as: S. Al-Agtash, Electricity agents in smart grid markets, Comput. Industry (2012), http://dx.doi.org/ 10.1016/j.compind.2012.10.009
G Model
COMIND-2397; No. of Pages 7 2
S. Al-Agtash / Computers in Industry xxx (2012) xxx–xxx
basis of the SCADA-EMS supervisory control and data acquisition management system. The architecture, however, simulates the complexity in the generic SCADA setup, which has not been yet recognized as viable platforms for the evolving smart grid system. The report of the European Commission – Directorate General for Research Sustainable Energy Systems envisions the settings of a new technology platform [19], that can support communication and coordination of data between a large variety of grid devices, including power production resources, network nodes, and local loads. It was reported that resources are numerous when considering a mix of green generation (e.g. Wind generators, fuel cells, photovoltaic panels, micro-turbines) and numerous demand side management options. In Ref. [20], the Directorate calls for a trans-European approach that provides new and better technical foundations for distant control of highly distributed networks on an increasingly large scale. The approach would involve new technologies for generation, networks, energy storage, load efficiency, control and communications, liberalized markets and environmental challenges, which can integrate and operate in a distributed environment. Because of the decentralized nature of the smart grid operation, coordination, and management, agent technologies emerge as viable tools for automation, operation, control, and monitoring of the whole integrated smart grid system. Ref. [12] reports on current trends for the use of automated agent technology in the control architecture of the smart grid. The authors discuss the design and implementation of agent intelligence in a distributed environment. The use of multi-agent system is justified by its distributed nature with intelligent and yet autonomous entities acting on behalf of the grid components. The specific problem domain lies in the capabilities of agents to control a distributed smart grid in different islands to avoid cascading failures. The results of this system design have shown that the proposed multiagent system can facilitate seamless transition to grid islands when outages are detected, and therefore, proves the capability of a multi-agent system as a technology for managing micro-grid operations. A future vision of an electricity distributed network capable of autonomous and intelligence configuration in the context of the iDEaS project is presented in [18]. It is based on the assumption that intelligent electricity supply networks can allow efficient use of energy resources, reduce emissions, and robustly operate. The authors argue that the distributed nature of the smart grid and its intelligent and autonomous behavior lend itself to a multi agent technology. It was demonstrated that this technology can realize monitoring, visualizing, and coordinating energy use, flatten demand across the grid, and finally can effectively and robustly operate the distributed grid system. Variety of other research has focused on multi-agent system technologies as modeling and simulation tools that may eventually evolve as the basis architecture for an agent-based smart grid. Ref. [9] provides insights on the capabilities of agents in real world electricity markets. It was argued that multi-agent technology fits better the ‘‘smart grid’’ problem domain when compared to discrete event simulation and distributed artificial intelligence models. This is mainly due to the ability of agent technology to find the set of basic decision rules and behavioral interactions that can produce the complex results and macro-scale consequences experienced in the real world. The unique problem domain lies in the design of agents as software components representing decision-making units. The decision making has been developed in agents to further accommodate transmission system limitations and estimation capabilities of electricity price fluctuations for the wholesale market [16,17]. The authors present comparisons of different ‘‘power system’’ agent tools and capabilities. Ref. [13] introduces a multi-agent simulator system as a valuable framework for evaluating new rules, new behavior, and new participants
in the numerous electricity markets that are moving toward liberalization and competition. The simulator makes use of Open Agent Architecture to create a rule-based system with no limitations on the transmission system. As agents continue to evolve as viable solutions to the complexity of smart grids, key design principles are still missing in the literature when it comes to the operational aspects and technology platforms of the diverse grid components to communicate and coordinate seamlessly in a computationally intensive integrated architecture. This paper contributes to developing such a computational architecture that allows electricity agents to coordinate in an integrated approach within a smart gird environment. Agents are designed to model ‘‘smart grid’’ elements in a two-way flow power market. A computational model is given to cope with demand variations and to accommodate selling of power at the customer side. Agent operations are automated to allow exploring a wider spectrum of trade opportunities in an efficient manner, while coordinating with the system operator for reliability, security, and stability. A software model is presented with standard agent plug-ins designed on the basis of portlets and implemented as part of the smart grid middleware architecture. Agents authenticate and gain access to different profiles. The agent functionality is designed to autonomously analyze large stacks of market data, explore alternative profitable trades on behalf of its underlying electricity component, and enhance operational performance of the whole grid system. This is a novel approach to agent modeling of ‘‘smart grid’’ components. A small-scale 5-bus ‘‘smart grid’’ system is used for testing. It is intended to illustrate benefits of smart grid and agent automation, such as low-cost energy, economic efficiency, reliability, and adequate power quality, when compared to the standard Cournot and Bertrand computational models of market competition. The remaining sections of this paper are organized as follows: Section 2 presents smart grid architecture. Section 3 gives details of power grid components and models. Grid middleware is presented in Section 4. Electricity agent framework is given in Section 5. Section 6 provides numerical testing of an IEEE 5-bus system. Finally, the paper is concluded in Section 7. 2. Smart grid architecture The existing power grids worldwide represent one-way electricity flow and aging ‘‘centralized topology’’ architectures. The grid’s communication system is considered slow in response and needs to be upgraded to allow two-way flow of electricity between generators and consumers, integrate advanced communication technologies, and incorporate IP based communications architecture. The smart grid architecture in consensus consists of three layers [10], and is shown in Fig. 1. One layer consists of the power grid generation, transmission, and distribution components. The second layer represents the Grid ‘‘middleware’’ interacting with all components. The third layer includes electricity agents that perform their activities, interact, and share data on behalf of their underlying generation, distribution, and transmission companies. The agents interact in a multi-agent system platform. The multi-agent system has several benefits as it provides a common communication interface, autonomous configuration of all elements, adaptable local control, and intelligent decision making. Details of these behavioral aspects are given in details in the design of the electricity agent framework in Section 5. The multi-agent system is introduced as an alternative to the currently used centralized SCADA (supervisory control and data acquisition) system. Even several smaller distributed SCADA systems will no longer be sufficient for the smart grid operations, as it lacks adaptability, intelligent decision making, and autonomy. Some of the key advantages of multi-agent system are: (1) uniform
Please cite this article in press as: S. Al-Agtash, Electricity agents in smart grid markets, Comput. Industry (2012), http://dx.doi.org/ 10.1016/j.compind.2012.10.009
G Model
COMIND-2397; No. of Pages 7 S. Al-Agtash / Computers in Industry xxx (2012) xxx–xxx
3
The cost of generation is given by Ck[gk(t)] including start-up and other costs. 3.2. ConCo
Fig. 1. Smart grid architecture.
communication platform, (2) decentralized nature of problem solving, (3) intelligent decision making, and (4) design features and capabilities including autonomy, flexibility, extensibility, fault tolerance, management controls. Communication over the grid is introduced in ‘‘open smart grid communications architecture’’ for private IP cloud connectivity across the whole spectrum of smart grid infrastructure. The grid architecture adds intelligence to its components to make it able to sense, monitor, and control their operations in an optimal and secure environment. 3. Power grid framework The power grid represents unbundled generation, transmission, and distribution components. In the ‘‘smart grid electricity market’’ setup, these are represented and operated by power generating and consuming companies (denoted respectively as GenCo and ConCo), transmission owners (TOs), a power exchange (PX), and an independent system operator (ISO). The GenCos and ConCos trade multilaterally or through bidding into the PX. The PX determines the winning bids, schedules generation resources, and coordinates with the ISO and TOs for transmission access. The main task of the ISO is to operate the system in real-time and maintains reliability and security. It coordinates with GenCos, ConCos, and TOs to maintain enough reserve of resources. 3.1. GenCo A GenCo is a power generating company that owns ng generating units in one location or in more than one location. Unit i injects electric power gi through its kth connected generation bus bk to the transmission network. It operates under a number of constraints, formally given by h(gi). GenCo agents compete to meet customer demand represented by ConCo agents. Competition takes place in a ‘‘smart grid electricity market’’ setup. GenCos are assumed to bid their available (not contracted) bus generation into PX over a planning horizon T. The jth GenCo denoted by Gj submits its bid as a supply curve given by Sj,k : r ! Sj,k(r) for its available generation at bus k, where r represents the market spot price. The curve describes desirable generation quantities for each price signal. The power generation at bus k, during hour t, is gk(t).
A ConCo is a power consuming company that buys power on behalf of its customers, facilitates distributed renewable generation through inverter devices, and coordinates power trades to the grid. Renewable generation is becoming widely used through small-scale technologies that produce low-cost, reliable, and clean electricity close to customers. Clean power is mainly generated through solar panels and wind turbines. ConCo agents predict power demand at each of its load bus. It submits a demand response curve at bus k during hour t as dm,k : (r, t) ! dm,k(r, t). The demand may be inelastic, i.e. fixed for any price signal. The response is chosen such that a customer maximizes its net benefits [14], and that in case of power surplus it submits bid Pk(t). The system total demand, during hour t, is computed as the sum of dm,k(r, t) and is given by D(r, t). Each ConCo is assumed to implement dynamic scheduling of dispatachable generation, demand-side management techniques, consolidation of load balances for separate power zones represented by at bus k, and flexible energy storage. Through renewable energy sources installed at distribution nodes, homes and offices, ConCos may buy clean power from customers, allowing them to take an active role in the electricity market. However, renewable resources are driven by nature, which is non-predictable. This poses a challenge in the complexity of load estimation at the demand side. Let pm,k(t) represents the estimated clean power made available at load bus k by the connected individual customers at time t. The clean power is generated from environment-friendly resources such as solar power, wind turbines, and other small scale emission free energy resources. The dispatch problem is formulated so that ConCo’s benefit function is maximized. With demand side management, ConCos reduces power consumption during peak hours by implementing ‘‘Load shedding’’ mechanisms through alerts of smart meters and variable pricing signals. 3.3. Electricity market GenCos and ConCos trade independently through multilateral negotiations, brokerage systems, or local power exchange entities. They seek profit on their own, determine their operational schedules, and commit to maintain reliability of the system. Several market mechanisms exist. In a PX setup, the market equilibrium during hour t is at the market clearing price where the demand curve D(r, t) crosses the aggregate supply curve S(r). The demand curve defines the demand of electric power as a function of time and price. The supply curve defines the supply of electric power as a function of price. These curves can be linear, monolithic, or non-linear depending on the behavioral characteristics of the consumers and producers of power in electricity markets. In a multilateral negotiation, each ConCo is assumed to submit its contract volume as a function of market price and other parameters so as to maximize its benefit. Each GenCo negotiates its selling price and associated contract parameters to maximize its profit. Details of market operations and model of competitions are available in variety of electricity market literature. 3.4. System operator The physical operation and control of the entire system in realtime is maintained by the ISO. Its primary function is to maintain [2]:
Please cite this article in press as: S. Al-Agtash, Electricity agents in smart grid markets, Comput. Industry (2012), http://dx.doi.org/ 10.1016/j.compind.2012.10.009
G Model
COMIND-2397; No. of Pages 7 S. Al-Agtash / Computers in Industry xxx (2012) xxx–xxx
4
-
P generation-load balance at price r, time t, k,tgk(t) = D(r, t) P enough spinning reserve r at bus k, time t, i,tgi(t) gk(t) rk(t) Min–max available bus generation, at time t gk,min gk(t) gk,max Flow limits on transmission lines: |z1(t)| = z1,max The ISO implements several tasks, including:
-
operation of the forward market for energy operation of the forward market for ancillary services dispatch of the physical system computing settlement payments to market participants
The ISO does not engage in any trades but operates the system in real-time. The ISO sets system reliability parameters including spinning reserves. It undertakes grid monitoring and control in real-time to support utility companies to avoid power outages as well as surveillance of substations, transformers, power lines, and other grid components through active monitoring. 4. Grid middleware In a smart grid environment, GenCo, ConCo, TO, PX, and ISO are operated by an automated and yet smart systems, denoted here as electricity agents. The agents perform their daily tasks on the basis of large amounts of market data in real-time. Agents interact, integrate and operate through grid middleware for uniformity. We propose a generic middleware architecture as depicted in Fig. 2. The middleware focuses on standard agent plug-ins at the presentation layer, restricted access at the authentication layer, and data exchange at the application layer. The presentation layer simplifies the management of information coming from plug-in extensions on the basis of portlets. The authentication layer is composed of options that restrict accessibility to different profiles of agents, possibly through digital certificates. The data layer represents abstraction to local and distributed data. Several design issues on layers still need further investigation, including: - Requirement of APIs (application programming interface) to accommodate different agent platforms implementing specialized and decoupled software components representing the underlying business entities - Handling semantic coherence, integrity and consistency of operations on agent application and data set
- Defining appropriate design patterns and architectures of agent systems 5. Electricity agent framework Agent-based modeling approach is being increasingly attracted through a wide range of applications, including electricity markets. We consider a grid system where there are a set of agents that represent GenCos, ConCos, TO, and PX denoted by G Agents, C Agents, T Agent, and P Agent respectively. Agents coordinate and operate in a smart grid electricity market. In the following subsections, we present the business and technical operation models of these agents. Agents are developed to adapt to the electricity market requirements and behave intelligently to maximize benefits on behalf of their underlying business entity. We assume that GenCos, ConCos, PX, and ISO trade and coordinate with each other over a dedicated ‘‘smart grid’’ infrastructure. An agent is designed to model each entity. It is a software system that implements business and operation activities of its underlying entity, interacts with other agents. Agents are installed as part of the third layer of the ‘‘smart grid’’ system. An agent represents the internal functionality of the entity’s main structural departments: production, marketing, procurement, finance, and strategy. Even though there is no common standard definition of an electricity agent, our agents possess several agreed-upon characteristics including autonomy, self-initiation, adaptability, persistence, collaboration, and evolution through learning. We propose a software hierarchical structure for the ‘‘smart grid’’ design and implementation. It consists of agents representing individual system components: generators, distributors, market and system operators, and transmission lines. Each of these agents has two components: state (physical characteristics, limitations, and capabilities) and behavior (business strategies, local and global interactions to maintain system operational requirements). An agent class maps the state of a respective component into a set of attributes and maps its behavior into methods. Agents that model components of the same type are instances of the same agent class. At a higher level, agent classes are arranged into a hierarchical structure. The software agents, at the same and different levels, interact and communicate with each other via special type of messages based on KQML (Knowledge Query Manipulation Language) to maintain secure and reliable overall system operation. These interactions are mapped into functional procedures reflecting agents’ intelligence to optimize
Fig. 2. Smart grid middleware.
Please cite this article in press as: S. Al-Agtash, Electricity agents in smart grid markets, Comput. Industry (2012), http://dx.doi.org/ 10.1016/j.compind.2012.10.009
G Model
COMIND-2397; No. of Pages 7 S. Al-Agtash / Computers in Industry xxx (2012) xxx–xxx
5
5.3. Agent behavior An agent reacts to a spectrum of events using embedded behavioral functions:
Fig. 3. Architecture of the software model.
performance. Agents react to local and global changes or signals subject to the various system constraints. We refer to changes, which are limited to an agent as local changes. Global changes, on the other hand, are changes of agent instance(s) that might affect other agents. Fig. 3 presents the proposed architecture of the software model. There are three agent classes: generation, transmission and distribution. A generator, for instance, continuously monitors system demand and rivals’ strategic behavior and accordingly determines its generation sales or bids. Market prices at the generation and load busses are globally negotiated until cleared. At equilibrium, generation and demand schedules, line flows and busclearing prices are determined, while taking into account the various system constraints. In real time, all agents must coordinate with the system operator to maintain line flows, system frequency and bus voltages within secure limits. Agents interact over the ‘‘smart grid infrastructure’’ through a common agent platform. 5.1. Agent class The agent class represents [3]: 1. Agent state defined by a tuple of attributes as T 2 R : T(x1, . . ., xn, ts), where R defines a table of state T and {xi}i=1:n denotes a set of n attributes for a time stamp ts. 2. Agent behavior implemented by a functional map set B : (T, E) ! B(T, E), where E defines a set of external events resulting from state changes of peer agents.
Trade: an operation that implements a specific trading protocol with the following functional activities: PX auction bidding: numerous bidding strategies has been proposed based on game theory [11] and genetic algorithms [12,15]. Multilateral contract: the main contracting strategies are based on the economic theory of oligopoly [3]. Coordinate: agents coordinate to meet transmission constraints and operational requirements, and to maintain security and reliability. Coordination parameters are: Strategy verification and validation so as to make sure that the system is secure and reliable. Trade coordination to compensate for network losses. Embedded intelligence to provide alternative strategies in system contingencies. Each of these behavioral functions is based on detailed mathematical formulations and are presented in [1]. 5.4. Agent platform The platform provides communication services between agents. It follows the general agent design guidelines and adhoc standards currently embraced by the agent community [4,16,18]. Its main components are management, directory service, and ACC (Agent Communication Channel). A management component keeps track of connected agents. A directory service keeps record of agents connected to the platform. The ACC implements communication with other agents. 5.5. Agent communication GenCo and ConCo agents use KQML (Knowledge Query Manipulation Language) as a standard for communication. It provides agents with a means of exchanging information and knowledge over the network using a lower-level TCP/IP protocol. KQML is message-oriented and content independent. A KQML message consists of three layers: content, communication, and message. The content layer represents the actual content of the message obtained in the program’s own representation language. The communication layer encodes a set of features to describe lower-level communication parameters. The message layer formulates the strategy message.
5.2. Agent state Some of the state attributes are common to all agents. These include: Agent identification, commercial identity, owner, agent type (GenCo, Conco, ISO, PX, or Broker), license, certification, and authorization. Other state attributes are specific to GenCo, ConCo, ISO and other business agents as defined in the Dictionary format of the IEEE Power System Applications Data (PSADD). In this format the specific attributes of a GenCo agent include 16 variable elements defining company generators’ power output, operating voltage and power limits, cost parameters, etc. The specific attributes of a ConCo agent, on the other hand, include 15 variable elements defining power load, min and max power and voltage limits, etc.
Fig. 4. KQML based agent communication.
Please cite this article in press as: S. Al-Agtash, Electricity agents in smart grid markets, Comput. Industry (2012), http://dx.doi.org/ 10.1016/j.compind.2012.10.009
G Model
COMIND-2397; No. of Pages 7 S. Al-Agtash / Computers in Industry xxx (2012) xxx–xxx
6
Fig. 4 shows the implementation of agent communication. To facilitate registration, name resolution and other functionalities that simplify access to the network, KQML Router Interface Library (KRIL) is implemented. The KRIL has a central function into the communication process. It retrieves incoming messages routed through the Router (steps 1, 2), converts them into actions via the KQML methods and databases access, executes basic functions such as agent declaration to the server (step 3), and transmits complex requests to the agent’s main application (step 4). Below is the code declaration, representing the interactions: Fig. 5. IEEE 5-bus system.
******************* #import "..\Routerv1\Debug\routerv1.dll" #import "..\KQMLMod\Debug\KQMLMod.dll" pRouterObj=NULL; hr = E_FAIL; hr =CLSIDFromProgID(OLESTR("Routerv1.RouterObj" ),&clsid); CoInitialize(NULL); //initialize the COM hr = CoCreateInstance( clsid, NULL, CLSCTX_ALL, __uuidof(IRouterObj), (void**)&pRouterObj); pKQMLObj=NULL; hr = E_FAIL; hr = CLSIDFromProgID(OLESTR("KQMLMod.KQMLObj" ),&clsid); hr = CoCreateInstance( clsid, NULL, CLSCTX_ALL, __uuidof(IKQMLObj), (void**)&pKQMLObj); Genco and ConCo agents use content specification to describe their requirements and constraints, which form the basis of the trading process. The syntax of the data and constraint specification part of the language is similar to existing object modeling languages (e.g., ODL, UML, etc.). Each item or service offered or to be acquired is represented as an entity, which has one or more attributes describing its properties and one or more constraints describing the attribute and inter-attribute constraints. The model is then translated into a KQML object and submitted to the corresponding agent. Agents in the system are auto-detectable. Once activated (turned on), it automatically recognizes all agents in its surrounding (grid) running on local or remote hosts, and notifies them of its presence. Authentication message rounds handle security and establish communication only with authorized agents that are registered and digitally authenticated. 5.6. Agent authentication The authentication between agents and grid may be implemented using public key infrastructure certificates. The main objectives are to: - To identify agents responsible for services which are acting on behalf of the underlying business entity - To authenticate the service to another electricity agent and perform some repetitive servicesTwo are fundamental characteristics of certificates: - One that is always related to a specific agent - Another that includes the agent subscriber and its application. Each time an agent tries to access the grid, a certificate will be requested to allow agents prove their identity. Using certificates, agents can interact with other agents on the grid. 6. Application example We consider the IEEE 5-bus system with 2 GenCos and 3 ConCos for illustration. Fig. 5 shows the grid system and with the specific
generation and load data. GenCo 1 injects power at bus 4 and is assumed to have a marginal cost function equal to 35 + 0.2 g1$/ MWh and 5–120 MW min–max values. GenCo 2 has a marginal cost function equal to 40 + 0.22 g2$/MWh and 5–80 MW min–max values. ConCo1 has a demand curve equal to 60–0.41d1$/MWh and 10–40 MW min–max values. ConCo2 has a demand curve equal to 60–0.42d2$/MWh and 10–60 MW min–max values. ConCo3 has a demand curve equal to 60–0.4d3$/MWh and 10–40 MW min–max values. 6.1. Software implementation The software implementation of the smart grid system is based on C++, with remote database access using ActiveX Data Objects (ADO), network communication using Winsock library, component object model (COM), and Win32 multithreading interface. Agent visual interfaces are designed for operators to be able to manage and set market and operational parameters. The business logic of each agent is embedded and implemented on a separate process. A shared-memory segment is used for communication and exchange of control and operational information between agent modules. Access synchronization to internal data sharing is implemented using semaphores in order to avoid race conditions. The agent main trading server sleeps on trading signals during idle intervals. The agent sends a wakeup signal to awaken its respective server before sending any request. Synchronized access to the database by multiple instances of the trading server is achieved by native database locks, which guarantee consistency and deadlock free operation. Multithreading is used to enhance concurrency in Agent operation and communication. The agent spawns separate threads to carry out trading and communication processes with peer agents as well as with the grid system. COM library is used to implement some of the common modules within the server and to implement KQML communication, for the sake of efficiency, standardization, and reusability. Operational data is deposited in a Database. Access to the data can be either local upon a request or remote through ActiveX data objects, which actually utilize the ADO (Active-X Data Objects) over ODBC (Open Database Connectivity) layer to allow access for authorized entities only. Authentication message rounds handle security and establishing communication only with authorized agents that are registered in the local security database. The tool allows the administrator to create and configure an agent depending on its type and parameters. Each element of the grid system, namely GenCo, ConCo, PX, and ISO are represented by the agent structure proposed in this paper. ConCos are assumed to submit their demand curves on daily basis and negotiate contracts of fixed 50 MW for 6-month time period. Equally, GenCos are assumed to submit their daily supply curves and negotiate requested contracts. The grid agent system has been simulated, with seven agents, each implements a business logic related to its functional domain.
Please cite this article in press as: S. Al-Agtash, Electricity agents in smart grid markets, Comput. Industry (2012), http://dx.doi.org/ 10.1016/j.compind.2012.10.009
G Model
COMIND-2397; No. of Pages 7 S. Al-Agtash / Computers in Industry xxx (2012) xxx–xxx
6.2. Preliminary numerical testing GenCos receive a request for market trade from PX and ConCos. GenCos and ConCos initiate threads handling separate trading processes (Contractual, day-ahead, reserve, etc.). The agents implement trading processes, which involve two-way power flow. We give results of simulation on different load profiles including contractual trades for six months, with market clearing prices that are variant ranging between 50$/MWh and 66.0$/MWh. It is assumed that ConCo 3 uses renewable energy sources and sell to the grid during some hours 10 MWh at the market clearing price, which drops down by 1% closer to the marginal costs as demand decreases. These results are far better that the results of traditional Cournot and Bertrand models of market competition. The market clearing prices in a Bertrand contractual and an oligopolistic Cournot market setup ranged between 60 and 80$/ MWh for different load profiles, much higher than the marginal costs of generation (40–55$/MWh). Therefore, smart grid with agents acting on behalf of their underlying business entities enhance efficiency at the economic levels as exploration of wider spectrum of trades enhances competition and therefore drive market clearing prices closer to marginal costs of generation. Reliability, security, and power quality can be further enhanced with intensive and timely communication using specialized IP networks among business entities as well as with the system operator. 7. Conclusion This paper presents an agent framework for the entities of the ‘‘smart grid’’ system. The framework allows standard agent plug-ins into the grid middleware. Authentication is maintained on a basis of digital certificates. Agent operations are automated to allow demand variations as well as explorations of a wider spectrum of trade opportunities in an efficient manner. Agents are implemented using C++ and use KQML based messages for communication. Agents coordinate with the system operator for reliability, security, and stability. The results of the small scale IEEE 5-bus grid system show important benefits such as coordination and automation, economic efficiency, reliability and security, and adequate power quality. It was obvious from a set of test cases on different load profiles that agent automation in a ‘‘smart grid’’ drive market clearing prices close to the marginal costs of power generation compared to the standard Cournot and Bertrand competition models. In continuation to our research in electricity systems, we plan to incorporate complexity of trades that involve more variables related to a mix of generation resources and realtime market requirements. References [1] S. Al-Agtash, N. Al-Fayoumi, A software architecture for modeling competitive power systems, in: Proceedings of the IEEE PES Winter Meeting, vol. 3, 2000, pp. 1674–1679.
7
[2] S. Al-Agtash, Supply curve bidding of electricity in constrained power networks, Energy 35 (7) (2010) 2886–2892. [3] N. Al-Fayoumi, S. Al-Agtash, Electronic multilateral trade of electricity, Advances in Engineering Software 33 (11–12) (2002) 749–759. [4] K. Barber, T. Liu, D. Han, in: F.J. Garijo, M. Boman (Eds.), Agent-Oriented Design, Multi-Agent System Engineering: Proceeding of the 9th European Workshop on Modeling Autonomous Agents in a Multi-Agent World, MAAMAW’99. Lecture Notes in Computer Science: Lecture Notes in Artificial Intelligence, Valencia, Spain, Springer, 1999, pp. 28–40. [5] A. Bose, Smart transmission grid applications and their supporting infrastructure, IEEE Transactions on Smart Grid 1 (1) (2010) 11–19. [6] W. Frye, Smart Grid Transforming the Electricity System to Meet Future Demand and Reduce Greenhouse Gas Emissions, Cisco Internet Business Solutions Group, 2008. [7] V. Giordano, G. Fulli, A business case for Smart Grid technologies: a systemic perspective, Energy Policy 40 (2012) 252–259. [8] P. Joskow, Creating a smarter U.S. electricity grid, Journal of Economic Perspectives 26 (1) (2012) 29–48. [9] V. Koritarov, Real-world market representation with agents, IEEE Power and Energy Magazine (2004) 39–46. [10] F. Li, W. Qiao, H. Sun, H. Wan, J. Wang, Y. Xia, Z. Xu, P. Zhang, Smart transmission grid: vision and framework, IEEE Transactions on Smart Grid 1 (2) (2010) 168– 177. [11] J. Lamont, S. Rajan, Strategic bidding in an energy brokerage, IEEE Transactions on Power Systems 12 (4) (1997) 1729–1733. [12] M. Pipattanasomporn, Multi-agent systems in a distributed smart grid: design and implementation, in: Proceedings of IEEE PES 2009 Power Systems Conference and Exposition, USA, 2009. [13] I. Praca, C. Ramos, Z. Vale, M. Cordeiro, MASCEM: a multiagent system that simulates competitive electricity markets, IEEE Intelligent Systems 18 (6) (2003) 54–60. [14] F. Rahimi, A. Ipakchi, Demand response as a market resource under the smart grid paradigm, IEEE Transactions on Smart Grid 1 (1) (2010) 82–88. [15] C. Richter, G. Sheble, D. Ashlock, Comprehensive bidding strategies with genetic programming/finite state automata, IEEE Transactions on Power Systems 14 (4) (1999) 1207–1212. [16] T. Sueyoshi, G. Tadiparthi, An agent-based decision support system for wholesale electricity market, Decision Support Systems 44 (2008) 425–446. [17] T. Sueyoshi, G. Tadiparthi, Wholesale power price dynamics under transmission line limits: a use of an agent-based intelligent simulator, IEEE Transactions on System, Man, Cybernetics Applications 38 (2) (2008) 229–241. [18] P. Vytelingum, Trading agents for the smart electricity grid, in: Proceedings of 9th International Conference on Autonomous Agents and Multi-agent Systems, Canada, May, 2010.
Further reading [19] Towards smart power networks, European Commission, 2005 [Online]. Available: http://ec.europa.eu/research/energy/pdf/towards_smartpower_en.pdf. [20] European Smart Grids Technology Platform, European Commission, 2006 [Online]. Available: http://ec.europa.eu/research/energy/pdf/smartgrids_en.pdf.
Salem Al-Agtash got his Ph.D. in Electrical Engineering from the University of Colorado at Boulder in 1998. He served as a department chair (2001–2003) and a managing director (2003–2005) at Yamouk University and a dean (2005–2009) at the German Jordanian University. Dr. Al-Agtash is currently an associate professor of computer engineering and the dean of the School of Information Technology and Engineering. He has worked on several research and international development projects, mainly with the World Bank, European Commission, JIKA, and USAID. He has been very active in developing relevant and quality ICT educational programs, strengthening tri-partite links between university, private sector and government, and building international cooperation in education, mainly in Europe. His research interests are in the areas of electricity industry, agent based systems, software engineering, and education management.
Please cite this article in press as: S. Al-Agtash, Electricity agents in smart grid markets, Comput. Industry (2012), http://dx.doi.org/ 10.1016/j.compind.2012.10.009
COMIND-2397; No. of Pages 7 Computers in Industry xxx (2012) xxx–xxx
Contents lists available at SciVerse ScienceDirect
Computers in Industry journal homepage: www.elsevier.com/locate/compind
Electricity agents in smart grid markets Salem Al-Agtash Department of Computer Engineering, German-Jordanian University, P.O. Box 35247, Amman 11180, Jordan
A R T I C L E I N F O
A B S T R A C T
Article history: Received 21 March 2012 Received in revised form 11 October 2012 Accepted 18 October 2012 Available online xxx
This paper presents a novel architecture of electricity agents in smart grid markets. The architecture implements a middleware that allows standard agent plug-ins representing ‘‘smart grid’’ elements in a two-way power flow. Agent operations are automated to allow demand variations and exploring a wider spectrum of trade opportunities in an efficient manner, while coordinating with the system operator for reliability, security, and stability. We give trade and operation computational models of power grid components and their interaction protocols and authentication in a multi-agent framework. Trade models are both pool and multilateral based. Operation models respect power dispatch limits, generation and transmission constraints, and spinning reserve requirements. The models are coded as part of the agent software. The protocols are KQML (Knowledge Query Manipulation Language) based communication. An IEEE 5-bus grid system is used for illustration. The testing results for different load profiles show better distribution of market sales and a market clearing price convergence very close to the marginal cost of generation supply, thus an implication of economic efficiency of energy resources, as well as security and power quality compared to traditional electricity trade models based on Cournot and Bertrand estimated clearing prices. ß 2012 Elsevier B.V. All rights reserved.
Keywords: Electricity markets Smart grid middleware Agent based modeling
1. Introduction Smart grid computing in power industry has become a trend, worldwide. The objective is to use smart technologies in today’s aging power grid for operation, control, and management, while unbundling generation, transmission, and distribution. This trend motivated new business models of electricity systems. Diverse electricity elements need to operate and interact in heterogeneous and yet decentralized decision structures. As power markets continue to evolve in a smart grid context, there is a growing consensus toward developing a sophisticated computing infrastructure. The infrastructure will have to accommodate complexity requirements of operation, interaction, and coordination of an integrated system with two-way electric power flow. The flow of power is not only from the grid to customers, but also from customers to the grid when customers have surplus of solar, wind, or any other renewable energy sources. The smart grid is widely recognized as the next generation electricity network [6–8]. It is based on communication and information infrastructure that can intelligently integrate and coordinate generation, distribution, and transmission. The smart grid is visualized to consist of three main components [6]: (1) demand management to monitor and manage consumption through smart meters, appliances, and programs; (2) distributed
E-mail address: [email protected].
electricity generation to accelerate widespread installation of renewable power sources; and (3) transmission and distribution grid management to monitor and control power grid in real time. In Ref. [7], it was argued that new policy interventions are needed for a systemic perspective to consolidate technical and economic synergies that allow individual smart grid technologies reverse the consumption-driven paradigm of the electricity sector. Opportunities, challenges, and uncertainties associated with the ‘‘smart grid’’ paradigm have been discussed in [8], with a focus on deployment issues of smart grid technologies at the high voltage transmission, local distribution, and end-use metering. The current technical research has focused in a great deal on developing supply bidding strategies, demand side management, locational marginal pricing, system congestion, and many other issues and scenarios of electricity markets. The ‘‘smart grid electricity system’’ adds more complexity to understanding, analyzing, and modeling of electric power industry. Complexity is in the communication and coordination between the diverse smart grid components that need to inter-operate and create a secure, economic, reliable, and yet an efficient smart electric power system [5,19,20]. In Ref. [5], the authors present the complexity of communication and coordination of huge data points representing every single control action and variable in every node in the whole integrated power system. The flow of data goes to/from the control center, the substations, automatic generation controls, and distribution bus systems. A real-time information architecture supported by high-speed networks has been introduced on the
0166-3615/$ – see front matter ß 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.compind.2012.10.009
Please cite this article in press as: S. Al-Agtash, Electricity agents in smart grid markets, Comput. Industry (2012), http://dx.doi.org/ 10.1016/j.compind.2012.10.009
G Model
COMIND-2397; No. of Pages 7 2
S. Al-Agtash / Computers in Industry xxx (2012) xxx–xxx
basis of the SCADA-EMS supervisory control and data acquisition management system. The architecture, however, simulates the complexity in the generic SCADA setup, which has not been yet recognized as viable platforms for the evolving smart grid system. The report of the European Commission – Directorate General for Research Sustainable Energy Systems envisions the settings of a new technology platform [19], that can support communication and coordination of data between a large variety of grid devices, including power production resources, network nodes, and local loads. It was reported that resources are numerous when considering a mix of green generation (e.g. Wind generators, fuel cells, photovoltaic panels, micro-turbines) and numerous demand side management options. In Ref. [20], the Directorate calls for a trans-European approach that provides new and better technical foundations for distant control of highly distributed networks on an increasingly large scale. The approach would involve new technologies for generation, networks, energy storage, load efficiency, control and communications, liberalized markets and environmental challenges, which can integrate and operate in a distributed environment. Because of the decentralized nature of the smart grid operation, coordination, and management, agent technologies emerge as viable tools for automation, operation, control, and monitoring of the whole integrated smart grid system. Ref. [12] reports on current trends for the use of automated agent technology in the control architecture of the smart grid. The authors discuss the design and implementation of agent intelligence in a distributed environment. The use of multi-agent system is justified by its distributed nature with intelligent and yet autonomous entities acting on behalf of the grid components. The specific problem domain lies in the capabilities of agents to control a distributed smart grid in different islands to avoid cascading failures. The results of this system design have shown that the proposed multiagent system can facilitate seamless transition to grid islands when outages are detected, and therefore, proves the capability of a multi-agent system as a technology for managing micro-grid operations. A future vision of an electricity distributed network capable of autonomous and intelligence configuration in the context of the iDEaS project is presented in [18]. It is based on the assumption that intelligent electricity supply networks can allow efficient use of energy resources, reduce emissions, and robustly operate. The authors argue that the distributed nature of the smart grid and its intelligent and autonomous behavior lend itself to a multi agent technology. It was demonstrated that this technology can realize monitoring, visualizing, and coordinating energy use, flatten demand across the grid, and finally can effectively and robustly operate the distributed grid system. Variety of other research has focused on multi-agent system technologies as modeling and simulation tools that may eventually evolve as the basis architecture for an agent-based smart grid. Ref. [9] provides insights on the capabilities of agents in real world electricity markets. It was argued that multi-agent technology fits better the ‘‘smart grid’’ problem domain when compared to discrete event simulation and distributed artificial intelligence models. This is mainly due to the ability of agent technology to find the set of basic decision rules and behavioral interactions that can produce the complex results and macro-scale consequences experienced in the real world. The unique problem domain lies in the design of agents as software components representing decision-making units. The decision making has been developed in agents to further accommodate transmission system limitations and estimation capabilities of electricity price fluctuations for the wholesale market [16,17]. The authors present comparisons of different ‘‘power system’’ agent tools and capabilities. Ref. [13] introduces a multi-agent simulator system as a valuable framework for evaluating new rules, new behavior, and new participants
in the numerous electricity markets that are moving toward liberalization and competition. The simulator makes use of Open Agent Architecture to create a rule-based system with no limitations on the transmission system. As agents continue to evolve as viable solutions to the complexity of smart grids, key design principles are still missing in the literature when it comes to the operational aspects and technology platforms of the diverse grid components to communicate and coordinate seamlessly in a computationally intensive integrated architecture. This paper contributes to developing such a computational architecture that allows electricity agents to coordinate in an integrated approach within a smart gird environment. Agents are designed to model ‘‘smart grid’’ elements in a two-way flow power market. A computational model is given to cope with demand variations and to accommodate selling of power at the customer side. Agent operations are automated to allow exploring a wider spectrum of trade opportunities in an efficient manner, while coordinating with the system operator for reliability, security, and stability. A software model is presented with standard agent plug-ins designed on the basis of portlets and implemented as part of the smart grid middleware architecture. Agents authenticate and gain access to different profiles. The agent functionality is designed to autonomously analyze large stacks of market data, explore alternative profitable trades on behalf of its underlying electricity component, and enhance operational performance of the whole grid system. This is a novel approach to agent modeling of ‘‘smart grid’’ components. A small-scale 5-bus ‘‘smart grid’’ system is used for testing. It is intended to illustrate benefits of smart grid and agent automation, such as low-cost energy, economic efficiency, reliability, and adequate power quality, when compared to the standard Cournot and Bertrand computational models of market competition. The remaining sections of this paper are organized as follows: Section 2 presents smart grid architecture. Section 3 gives details of power grid components and models. Grid middleware is presented in Section 4. Electricity agent framework is given in Section 5. Section 6 provides numerical testing of an IEEE 5-bus system. Finally, the paper is concluded in Section 7. 2. Smart grid architecture The existing power grids worldwide represent one-way electricity flow and aging ‘‘centralized topology’’ architectures. The grid’s communication system is considered slow in response and needs to be upgraded to allow two-way flow of electricity between generators and consumers, integrate advanced communication technologies, and incorporate IP based communications architecture. The smart grid architecture in consensus consists of three layers [10], and is shown in Fig. 1. One layer consists of the power grid generation, transmission, and distribution components. The second layer represents the Grid ‘‘middleware’’ interacting with all components. The third layer includes electricity agents that perform their activities, interact, and share data on behalf of their underlying generation, distribution, and transmission companies. The agents interact in a multi-agent system platform. The multi-agent system has several benefits as it provides a common communication interface, autonomous configuration of all elements, adaptable local control, and intelligent decision making. Details of these behavioral aspects are given in details in the design of the electricity agent framework in Section 5. The multi-agent system is introduced as an alternative to the currently used centralized SCADA (supervisory control and data acquisition) system. Even several smaller distributed SCADA systems will no longer be sufficient for the smart grid operations, as it lacks adaptability, intelligent decision making, and autonomy. Some of the key advantages of multi-agent system are: (1) uniform
Please cite this article in press as: S. Al-Agtash, Electricity agents in smart grid markets, Comput. Industry (2012), http://dx.doi.org/ 10.1016/j.compind.2012.10.009
G Model
COMIND-2397; No. of Pages 7 S. Al-Agtash / Computers in Industry xxx (2012) xxx–xxx
3
The cost of generation is given by Ck[gk(t)] including start-up and other costs. 3.2. ConCo
Fig. 1. Smart grid architecture.
communication platform, (2) decentralized nature of problem solving, (3) intelligent decision making, and (4) design features and capabilities including autonomy, flexibility, extensibility, fault tolerance, management controls. Communication over the grid is introduced in ‘‘open smart grid communications architecture’’ for private IP cloud connectivity across the whole spectrum of smart grid infrastructure. The grid architecture adds intelligence to its components to make it able to sense, monitor, and control their operations in an optimal and secure environment. 3. Power grid framework The power grid represents unbundled generation, transmission, and distribution components. In the ‘‘smart grid electricity market’’ setup, these are represented and operated by power generating and consuming companies (denoted respectively as GenCo and ConCo), transmission owners (TOs), a power exchange (PX), and an independent system operator (ISO). The GenCos and ConCos trade multilaterally or through bidding into the PX. The PX determines the winning bids, schedules generation resources, and coordinates with the ISO and TOs for transmission access. The main task of the ISO is to operate the system in real-time and maintains reliability and security. It coordinates with GenCos, ConCos, and TOs to maintain enough reserve of resources. 3.1. GenCo A GenCo is a power generating company that owns ng generating units in one location or in more than one location. Unit i injects electric power gi through its kth connected generation bus bk to the transmission network. It operates under a number of constraints, formally given by h(gi). GenCo agents compete to meet customer demand represented by ConCo agents. Competition takes place in a ‘‘smart grid electricity market’’ setup. GenCos are assumed to bid their available (not contracted) bus generation into PX over a planning horizon T. The jth GenCo denoted by Gj submits its bid as a supply curve given by Sj,k : r ! Sj,k(r) for its available generation at bus k, where r represents the market spot price. The curve describes desirable generation quantities for each price signal. The power generation at bus k, during hour t, is gk(t).
A ConCo is a power consuming company that buys power on behalf of its customers, facilitates distributed renewable generation through inverter devices, and coordinates power trades to the grid. Renewable generation is becoming widely used through small-scale technologies that produce low-cost, reliable, and clean electricity close to customers. Clean power is mainly generated through solar panels and wind turbines. ConCo agents predict power demand at each of its load bus. It submits a demand response curve at bus k during hour t as dm,k : (r, t) ! dm,k(r, t). The demand may be inelastic, i.e. fixed for any price signal. The response is chosen such that a customer maximizes its net benefits [14], and that in case of power surplus it submits bid Pk(t). The system total demand, during hour t, is computed as the sum of dm,k(r, t) and is given by D(r, t). Each ConCo is assumed to implement dynamic scheduling of dispatachable generation, demand-side management techniques, consolidation of load balances for separate power zones represented by at bus k, and flexible energy storage. Through renewable energy sources installed at distribution nodes, homes and offices, ConCos may buy clean power from customers, allowing them to take an active role in the electricity market. However, renewable resources are driven by nature, which is non-predictable. This poses a challenge in the complexity of load estimation at the demand side. Let pm,k(t) represents the estimated clean power made available at load bus k by the connected individual customers at time t. The clean power is generated from environment-friendly resources such as solar power, wind turbines, and other small scale emission free energy resources. The dispatch problem is formulated so that ConCo’s benefit function is maximized. With demand side management, ConCos reduces power consumption during peak hours by implementing ‘‘Load shedding’’ mechanisms through alerts of smart meters and variable pricing signals. 3.3. Electricity market GenCos and ConCos trade independently through multilateral negotiations, brokerage systems, or local power exchange entities. They seek profit on their own, determine their operational schedules, and commit to maintain reliability of the system. Several market mechanisms exist. In a PX setup, the market equilibrium during hour t is at the market clearing price where the demand curve D(r, t) crosses the aggregate supply curve S(r). The demand curve defines the demand of electric power as a function of time and price. The supply curve defines the supply of electric power as a function of price. These curves can be linear, monolithic, or non-linear depending on the behavioral characteristics of the consumers and producers of power in electricity markets. In a multilateral negotiation, each ConCo is assumed to submit its contract volume as a function of market price and other parameters so as to maximize its benefit. Each GenCo negotiates its selling price and associated contract parameters to maximize its profit. Details of market operations and model of competitions are available in variety of electricity market literature. 3.4. System operator The physical operation and control of the entire system in realtime is maintained by the ISO. Its primary function is to maintain [2]:
Please cite this article in press as: S. Al-Agtash, Electricity agents in smart grid markets, Comput. Industry (2012), http://dx.doi.org/ 10.1016/j.compind.2012.10.009
G Model
COMIND-2397; No. of Pages 7 S. Al-Agtash / Computers in Industry xxx (2012) xxx–xxx
4
-
P generation-load balance at price r, time t, k,tgk(t) = D(r, t) P enough spinning reserve r at bus k, time t, i,tgi(t) gk(t) rk(t) Min–max available bus generation, at time t gk,min gk(t) gk,max Flow limits on transmission lines: |z1(t)| = z1,max The ISO implements several tasks, including:
-
operation of the forward market for energy operation of the forward market for ancillary services dispatch of the physical system computing settlement payments to market participants
The ISO does not engage in any trades but operates the system in real-time. The ISO sets system reliability parameters including spinning reserves. It undertakes grid monitoring and control in real-time to support utility companies to avoid power outages as well as surveillance of substations, transformers, power lines, and other grid components through active monitoring. 4. Grid middleware In a smart grid environment, GenCo, ConCo, TO, PX, and ISO are operated by an automated and yet smart systems, denoted here as electricity agents. The agents perform their daily tasks on the basis of large amounts of market data in real-time. Agents interact, integrate and operate through grid middleware for uniformity. We propose a generic middleware architecture as depicted in Fig. 2. The middleware focuses on standard agent plug-ins at the presentation layer, restricted access at the authentication layer, and data exchange at the application layer. The presentation layer simplifies the management of information coming from plug-in extensions on the basis of portlets. The authentication layer is composed of options that restrict accessibility to different profiles of agents, possibly through digital certificates. The data layer represents abstraction to local and distributed data. Several design issues on layers still need further investigation, including: - Requirement of APIs (application programming interface) to accommodate different agent platforms implementing specialized and decoupled software components representing the underlying business entities - Handling semantic coherence, integrity and consistency of operations on agent application and data set
- Defining appropriate design patterns and architectures of agent systems 5. Electricity agent framework Agent-based modeling approach is being increasingly attracted through a wide range of applications, including electricity markets. We consider a grid system where there are a set of agents that represent GenCos, ConCos, TO, and PX denoted by G Agents, C Agents, T Agent, and P Agent respectively. Agents coordinate and operate in a smart grid electricity market. In the following subsections, we present the business and technical operation models of these agents. Agents are developed to adapt to the electricity market requirements and behave intelligently to maximize benefits on behalf of their underlying business entity. We assume that GenCos, ConCos, PX, and ISO trade and coordinate with each other over a dedicated ‘‘smart grid’’ infrastructure. An agent is designed to model each entity. It is a software system that implements business and operation activities of its underlying entity, interacts with other agents. Agents are installed as part of the third layer of the ‘‘smart grid’’ system. An agent represents the internal functionality of the entity’s main structural departments: production, marketing, procurement, finance, and strategy. Even though there is no common standard definition of an electricity agent, our agents possess several agreed-upon characteristics including autonomy, self-initiation, adaptability, persistence, collaboration, and evolution through learning. We propose a software hierarchical structure for the ‘‘smart grid’’ design and implementation. It consists of agents representing individual system components: generators, distributors, market and system operators, and transmission lines. Each of these agents has two components: state (physical characteristics, limitations, and capabilities) and behavior (business strategies, local and global interactions to maintain system operational requirements). An agent class maps the state of a respective component into a set of attributes and maps its behavior into methods. Agents that model components of the same type are instances of the same agent class. At a higher level, agent classes are arranged into a hierarchical structure. The software agents, at the same and different levels, interact and communicate with each other via special type of messages based on KQML (Knowledge Query Manipulation Language) to maintain secure and reliable overall system operation. These interactions are mapped into functional procedures reflecting agents’ intelligence to optimize
Fig. 2. Smart grid middleware.
Please cite this article in press as: S. Al-Agtash, Electricity agents in smart grid markets, Comput. Industry (2012), http://dx.doi.org/ 10.1016/j.compind.2012.10.009
G Model
COMIND-2397; No. of Pages 7 S. Al-Agtash / Computers in Industry xxx (2012) xxx–xxx
5
5.3. Agent behavior An agent reacts to a spectrum of events using embedded behavioral functions:
Fig. 3. Architecture of the software model.
performance. Agents react to local and global changes or signals subject to the various system constraints. We refer to changes, which are limited to an agent as local changes. Global changes, on the other hand, are changes of agent instance(s) that might affect other agents. Fig. 3 presents the proposed architecture of the software model. There are three agent classes: generation, transmission and distribution. A generator, for instance, continuously monitors system demand and rivals’ strategic behavior and accordingly determines its generation sales or bids. Market prices at the generation and load busses are globally negotiated until cleared. At equilibrium, generation and demand schedules, line flows and busclearing prices are determined, while taking into account the various system constraints. In real time, all agents must coordinate with the system operator to maintain line flows, system frequency and bus voltages within secure limits. Agents interact over the ‘‘smart grid infrastructure’’ through a common agent platform. 5.1. Agent class The agent class represents [3]: 1. Agent state defined by a tuple of attributes as T 2 R : T(x1, . . ., xn, ts), where R defines a table of state T and {xi}i=1:n denotes a set of n attributes for a time stamp ts. 2. Agent behavior implemented by a functional map set B : (T, E) ! B(T, E), where E defines a set of external events resulting from state changes of peer agents.
Trade: an operation that implements a specific trading protocol with the following functional activities: PX auction bidding: numerous bidding strategies has been proposed based on game theory [11] and genetic algorithms [12,15]. Multilateral contract: the main contracting strategies are based on the economic theory of oligopoly [3]. Coordinate: agents coordinate to meet transmission constraints and operational requirements, and to maintain security and reliability. Coordination parameters are: Strategy verification and validation so as to make sure that the system is secure and reliable. Trade coordination to compensate for network losses. Embedded intelligence to provide alternative strategies in system contingencies. Each of these behavioral functions is based on detailed mathematical formulations and are presented in [1]. 5.4. Agent platform The platform provides communication services between agents. It follows the general agent design guidelines and adhoc standards currently embraced by the agent community [4,16,18]. Its main components are management, directory service, and ACC (Agent Communication Channel). A management component keeps track of connected agents. A directory service keeps record of agents connected to the platform. The ACC implements communication with other agents. 5.5. Agent communication GenCo and ConCo agents use KQML (Knowledge Query Manipulation Language) as a standard for communication. It provides agents with a means of exchanging information and knowledge over the network using a lower-level TCP/IP protocol. KQML is message-oriented and content independent. A KQML message consists of three layers: content, communication, and message. The content layer represents the actual content of the message obtained in the program’s own representation language. The communication layer encodes a set of features to describe lower-level communication parameters. The message layer formulates the strategy message.
5.2. Agent state Some of the state attributes are common to all agents. These include: Agent identification, commercial identity, owner, agent type (GenCo, Conco, ISO, PX, or Broker), license, certification, and authorization. Other state attributes are specific to GenCo, ConCo, ISO and other business agents as defined in the Dictionary format of the IEEE Power System Applications Data (PSADD). In this format the specific attributes of a GenCo agent include 16 variable elements defining company generators’ power output, operating voltage and power limits, cost parameters, etc. The specific attributes of a ConCo agent, on the other hand, include 15 variable elements defining power load, min and max power and voltage limits, etc.
Fig. 4. KQML based agent communication.
Please cite this article in press as: S. Al-Agtash, Electricity agents in smart grid markets, Comput. Industry (2012), http://dx.doi.org/ 10.1016/j.compind.2012.10.009
G Model
COMIND-2397; No. of Pages 7 S. Al-Agtash / Computers in Industry xxx (2012) xxx–xxx
6
Fig. 4 shows the implementation of agent communication. To facilitate registration, name resolution and other functionalities that simplify access to the network, KQML Router Interface Library (KRIL) is implemented. The KRIL has a central function into the communication process. It retrieves incoming messages routed through the Router (steps 1, 2), converts them into actions via the KQML methods and databases access, executes basic functions such as agent declaration to the server (step 3), and transmits complex requests to the agent’s main application (step 4). Below is the code declaration, representing the interactions: Fig. 5. IEEE 5-bus system.
******************* #import "..\Routerv1\Debug\routerv1.dll" #import "..\KQMLMod\Debug\KQMLMod.dll" pRouterObj=NULL; hr = E_FAIL; hr =CLSIDFromProgID(OLESTR("Routerv1.RouterObj" ),&clsid); CoInitialize(NULL); //initialize the COM hr = CoCreateInstance( clsid, NULL, CLSCTX_ALL, __uuidof(IRouterObj), (void**)&pRouterObj); pKQMLObj=NULL; hr = E_FAIL; hr = CLSIDFromProgID(OLESTR("KQMLMod.KQMLObj" ),&clsid); hr = CoCreateInstance( clsid, NULL, CLSCTX_ALL, __uuidof(IKQMLObj), (void**)&pKQMLObj); Genco and ConCo agents use content specification to describe their requirements and constraints, which form the basis of the trading process. The syntax of the data and constraint specification part of the language is similar to existing object modeling languages (e.g., ODL, UML, etc.). Each item or service offered or to be acquired is represented as an entity, which has one or more attributes describing its properties and one or more constraints describing the attribute and inter-attribute constraints. The model is then translated into a KQML object and submitted to the corresponding agent. Agents in the system are auto-detectable. Once activated (turned on), it automatically recognizes all agents in its surrounding (grid) running on local or remote hosts, and notifies them of its presence. Authentication message rounds handle security and establish communication only with authorized agents that are registered and digitally authenticated. 5.6. Agent authentication The authentication between agents and grid may be implemented using public key infrastructure certificates. The main objectives are to: - To identify agents responsible for services which are acting on behalf of the underlying business entity - To authenticate the service to another electricity agent and perform some repetitive servicesTwo are fundamental characteristics of certificates: - One that is always related to a specific agent - Another that includes the agent subscriber and its application. Each time an agent tries to access the grid, a certificate will be requested to allow agents prove their identity. Using certificates, agents can interact with other agents on the grid. 6. Application example We consider the IEEE 5-bus system with 2 GenCos and 3 ConCos for illustration. Fig. 5 shows the grid system and with the specific
generation and load data. GenCo 1 injects power at bus 4 and is assumed to have a marginal cost function equal to 35 + 0.2 g1$/ MWh and 5–120 MW min–max values. GenCo 2 has a marginal cost function equal to 40 + 0.22 g2$/MWh and 5–80 MW min–max values. ConCo1 has a demand curve equal to 60–0.41d1$/MWh and 10–40 MW min–max values. ConCo2 has a demand curve equal to 60–0.42d2$/MWh and 10–60 MW min–max values. ConCo3 has a demand curve equal to 60–0.4d3$/MWh and 10–40 MW min–max values. 6.1. Software implementation The software implementation of the smart grid system is based on C++, with remote database access using ActiveX Data Objects (ADO), network communication using Winsock library, component object model (COM), and Win32 multithreading interface. Agent visual interfaces are designed for operators to be able to manage and set market and operational parameters. The business logic of each agent is embedded and implemented on a separate process. A shared-memory segment is used for communication and exchange of control and operational information between agent modules. Access synchronization to internal data sharing is implemented using semaphores in order to avoid race conditions. The agent main trading server sleeps on trading signals during idle intervals. The agent sends a wakeup signal to awaken its respective server before sending any request. Synchronized access to the database by multiple instances of the trading server is achieved by native database locks, which guarantee consistency and deadlock free operation. Multithreading is used to enhance concurrency in Agent operation and communication. The agent spawns separate threads to carry out trading and communication processes with peer agents as well as with the grid system. COM library is used to implement some of the common modules within the server and to implement KQML communication, for the sake of efficiency, standardization, and reusability. Operational data is deposited in a Database. Access to the data can be either local upon a request or remote through ActiveX data objects, which actually utilize the ADO (Active-X Data Objects) over ODBC (Open Database Connectivity) layer to allow access for authorized entities only. Authentication message rounds handle security and establishing communication only with authorized agents that are registered in the local security database. The tool allows the administrator to create and configure an agent depending on its type and parameters. Each element of the grid system, namely GenCo, ConCo, PX, and ISO are represented by the agent structure proposed in this paper. ConCos are assumed to submit their demand curves on daily basis and negotiate contracts of fixed 50 MW for 6-month time period. Equally, GenCos are assumed to submit their daily supply curves and negotiate requested contracts. The grid agent system has been simulated, with seven agents, each implements a business logic related to its functional domain.
Please cite this article in press as: S. Al-Agtash, Electricity agents in smart grid markets, Comput. Industry (2012), http://dx.doi.org/ 10.1016/j.compind.2012.10.009
G Model
COMIND-2397; No. of Pages 7 S. Al-Agtash / Computers in Industry xxx (2012) xxx–xxx
6.2. Preliminary numerical testing GenCos receive a request for market trade from PX and ConCos. GenCos and ConCos initiate threads handling separate trading processes (Contractual, day-ahead, reserve, etc.). The agents implement trading processes, which involve two-way power flow. We give results of simulation on different load profiles including contractual trades for six months, with market clearing prices that are variant ranging between 50$/MWh and 66.0$/MWh. It is assumed that ConCo 3 uses renewable energy sources and sell to the grid during some hours 10 MWh at the market clearing price, which drops down by 1% closer to the marginal costs as demand decreases. These results are far better that the results of traditional Cournot and Bertrand models of market competition. The market clearing prices in a Bertrand contractual and an oligopolistic Cournot market setup ranged between 60 and 80$/ MWh for different load profiles, much higher than the marginal costs of generation (40–55$/MWh). Therefore, smart grid with agents acting on behalf of their underlying business entities enhance efficiency at the economic levels as exploration of wider spectrum of trades enhances competition and therefore drive market clearing prices closer to marginal costs of generation. Reliability, security, and power quality can be further enhanced with intensive and timely communication using specialized IP networks among business entities as well as with the system operator. 7. Conclusion This paper presents an agent framework for the entities of the ‘‘smart grid’’ system. The framework allows standard agent plug-ins into the grid middleware. Authentication is maintained on a basis of digital certificates. Agent operations are automated to allow demand variations as well as explorations of a wider spectrum of trade opportunities in an efficient manner. Agents are implemented using C++ and use KQML based messages for communication. Agents coordinate with the system operator for reliability, security, and stability. The results of the small scale IEEE 5-bus grid system show important benefits such as coordination and automation, economic efficiency, reliability and security, and adequate power quality. It was obvious from a set of test cases on different load profiles that agent automation in a ‘‘smart grid’’ drive market clearing prices close to the marginal costs of power generation compared to the standard Cournot and Bertrand competition models. In continuation to our research in electricity systems, we plan to incorporate complexity of trades that involve more variables related to a mix of generation resources and realtime market requirements. References [1] S. Al-Agtash, N. Al-Fayoumi, A software architecture for modeling competitive power systems, in: Proceedings of the IEEE PES Winter Meeting, vol. 3, 2000, pp. 1674–1679.
7
[2] S. Al-Agtash, Supply curve bidding of electricity in constrained power networks, Energy 35 (7) (2010) 2886–2892. [3] N. Al-Fayoumi, S. Al-Agtash, Electronic multilateral trade of electricity, Advances in Engineering Software 33 (11–12) (2002) 749–759. [4] K. Barber, T. Liu, D. Han, in: F.J. Garijo, M. Boman (Eds.), Agent-Oriented Design, Multi-Agent System Engineering: Proceeding of the 9th European Workshop on Modeling Autonomous Agents in a Multi-Agent World, MAAMAW’99. Lecture Notes in Computer Science: Lecture Notes in Artificial Intelligence, Valencia, Spain, Springer, 1999, pp. 28–40. [5] A. Bose, Smart transmission grid applications and their supporting infrastructure, IEEE Transactions on Smart Grid 1 (1) (2010) 11–19. [6] W. Frye, Smart Grid Transforming the Electricity System to Meet Future Demand and Reduce Greenhouse Gas Emissions, Cisco Internet Business Solutions Group, 2008. [7] V. Giordano, G. Fulli, A business case for Smart Grid technologies: a systemic perspective, Energy Policy 40 (2012) 252–259. [8] P. Joskow, Creating a smarter U.S. electricity grid, Journal of Economic Perspectives 26 (1) (2012) 29–48. [9] V. Koritarov, Real-world market representation with agents, IEEE Power and Energy Magazine (2004) 39–46. [10] F. Li, W. Qiao, H. Sun, H. Wan, J. Wang, Y. Xia, Z. Xu, P. Zhang, Smart transmission grid: vision and framework, IEEE Transactions on Smart Grid 1 (2) (2010) 168– 177. [11] J. Lamont, S. Rajan, Strategic bidding in an energy brokerage, IEEE Transactions on Power Systems 12 (4) (1997) 1729–1733. [12] M. Pipattanasomporn, Multi-agent systems in a distributed smart grid: design and implementation, in: Proceedings of IEEE PES 2009 Power Systems Conference and Exposition, USA, 2009. [13] I. Praca, C. Ramos, Z. Vale, M. Cordeiro, MASCEM: a multiagent system that simulates competitive electricity markets, IEEE Intelligent Systems 18 (6) (2003) 54–60. [14] F. Rahimi, A. Ipakchi, Demand response as a market resource under the smart grid paradigm, IEEE Transactions on Smart Grid 1 (1) (2010) 82–88. [15] C. Richter, G. Sheble, D. Ashlock, Comprehensive bidding strategies with genetic programming/finite state automata, IEEE Transactions on Power Systems 14 (4) (1999) 1207–1212. [16] T. Sueyoshi, G. Tadiparthi, An agent-based decision support system for wholesale electricity market, Decision Support Systems 44 (2008) 425–446. [17] T. Sueyoshi, G. Tadiparthi, Wholesale power price dynamics under transmission line limits: a use of an agent-based intelligent simulator, IEEE Transactions on System, Man, Cybernetics Applications 38 (2) (2008) 229–241. [18] P. Vytelingum, Trading agents for the smart electricity grid, in: Proceedings of 9th International Conference on Autonomous Agents and Multi-agent Systems, Canada, May, 2010.
Further reading [19] Towards smart power networks, European Commission, 2005 [Online]. Available: http://ec.europa.eu/research/energy/pdf/towards_smartpower_en.pdf. [20] European Smart Grids Technology Platform, European Commission, 2006 [Online]. Available: http://ec.europa.eu/research/energy/pdf/smartgrids_en.pdf.
Salem Al-Agtash got his Ph.D. in Electrical Engineering from the University of Colorado at Boulder in 1998. He served as a department chair (2001–2003) and a managing director (2003–2005) at Yamouk University and a dean (2005–2009) at the German Jordanian University. Dr. Al-Agtash is currently an associate professor of computer engineering and the dean of the School of Information Technology and Engineering. He has worked on several research and international development projects, mainly with the World Bank, European Commission, JIKA, and USAID. He has been very active in developing relevant and quality ICT educational programs, strengthening tri-partite links between university, private sector and government, and building international cooperation in education, mainly in Europe. His research interests are in the areas of electricity industry, agent based systems, software engineering, and education management.
Please cite this article in press as: S. Al-Agtash, Electricity agents in smart grid markets, Comput. Industry (2012), http://dx.doi.org/ 10.1016/j.compind.2012.10.009